{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":45867,"databundleVersionId":6924515,"sourceType":"competition"}],"dockerImageVersionId":30558,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# UBC Ovarian Cancer Subtype Classification and Outlier Detection (UBC-OCEAN)","metadata":{}},{"cell_type":"markdown","source":"### Import Libraries","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.applications import EfficientNetB0\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Dense, Flatten, Dropout, GlobalAveragePooling2D\nfrom tensorflow.keras.optimizers import Adam\n\nnum_classes = 5 # 5-class classification\n\n# Load the pre-trained EfficientNetB0 model\nbase_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(128, 128, 3))\n\n# **Fix: Use GlobalAveragePooling2D instead of Flatten**\nx = GlobalAveragePooling2D()(base_model.output)\nx = Dense(512, activation='relu')(x)\nx = Dropout(0.3)(x) # Adding dropout to prevent overfitting\nx = Dense(256, activation='relu')(x)\nx = Dropout(0.3)(x)\noutput = Dense(num_classes, activation='softmax')(x)\n\n# **Fix: Ensure 'outputs=output'**\nmodel = Model(inputs=base_model.input, outputs=output)\n\n# Unfreeze the last 20 layers for fine-tuning\nfor layer in base_model.layers[-20:]:\n layer.trainable = True\n\n# Compile the model with a learning rate schedule\nmodel.compile(optimizer=Adam(learning_rate=0.0001),\n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\nmodel.summary()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-11T07:20:04.820809Z","iopub.execute_input":"2025-03-11T07:20:04.82122Z"}},"outputs":[{"name":"stdout","text":"Model: \"model_1\"\n__________________________________________________________________________________________________\n Layer (type) Output Shape Param # Connected to \n==================================================================================================\n input_2 (InputLayer) [(None, 128, 128, 3 0 [] \n )] \n \n rescaling_2 (Rescaling) (None, 128, 128, 3) 0 ['input_2[0][0]'] \n \n normalization_1 (Normalization (None, 128, 128, 3) 7 ['rescaling_2[0][0]'] \n ) \n \n rescaling_3 (Rescaling) (None, 128, 128, 3) 0 ['normalization_1[0][0]'] \n \n stem_conv_pad (ZeroPadding2D) (None, 129, 129, 3) 0 ['rescaling_3[0][0]'] \n \n stem_conv (Conv2D) (None, 64, 64, 32) 864 ['stem_conv_pad[0][0]'] \n \n stem_bn (BatchNormalization) (None, 64, 64, 32) 128 ['stem_conv[0][0]'] \n \n stem_activation (Activation) (None, 64, 64, 32) 0 ['stem_bn[0][0]'] \n \n block1a_dwconv (DepthwiseConv2 (None, 64, 64, 32) 288 ['stem_activation[0][0]'] \n D) \n \n block1a_bn (BatchNormalization (None, 64, 64, 32) 128 ['block1a_dwconv[0][0]'] \n ) \n \n block1a_activation (Activation (None, 64, 64, 32) 0 ['block1a_bn[0][0]'] \n ) \n \n block1a_se_squeeze (GlobalAver (None, 32) 0 ['block1a_activation[0][0]'] \n agePooling2D) \n \n block1a_se_reshape (Reshape) (None, 1, 1, 32) 0 ['block1a_se_squeeze[0][0]'] \n \n block1a_se_reduce (Conv2D) (None, 1, 1, 8) 264 ['block1a_se_reshape[0][0]'] \n \n block1a_se_expand (Conv2D) (None, 1, 1, 32) 288 ['block1a_se_reduce[0][0]'] \n \n block1a_se_excite (Multiply) (None, 64, 64, 32) 0 ['block1a_activation[0][0]', \n 'block1a_se_expand[0][0]'] \n \n block1a_project_conv (Conv2D) (None, 64, 64, 16) 512 ['block1a_se_excite[0][0]'] \n \n block1a_project_bn (BatchNorma (None, 64, 64, 16) 64 ['block1a_project_conv[0][0]'] \n lization) \n \n block2a_expand_conv (Conv2D) (None, 64, 64, 96) 1536 ['block1a_project_bn[0][0]'] \n \n block2a_expand_bn (BatchNormal (None, 64, 64, 96) 384 ['block2a_expand_conv[0][0]'] \n ization) \n \n block2a_expand_activation (Act (None, 64, 64, 96) 0 ['block2a_expand_bn[0][0]'] \n ivation) \n \n block2a_dwconv_pad (ZeroPaddin (None, 65, 65, 96) 0 ['block2a_expand_activation[0][0]\n g2D) '] \n \n block2a_dwconv (DepthwiseConv2 (None, 32, 32, 96) 864 ['block2a_dwconv_pad[0][0]'] \n D) \n \n block2a_bn (BatchNormalization (None, 32, 32, 96) 384 ['block2a_dwconv[0][0]'] \n ) \n \n block2a_activation (Activation (None, 32, 32, 96) 0 ['block2a_bn[0][0]'] \n ) \n \n block2a_se_squeeze (GlobalAver (None, 96) 0 ['block2a_activation[0][0]'] \n agePooling2D) \n \n block2a_se_reshape (Reshape) (None, 1, 1, 96) 0 ['block2a_se_squeeze[0][0]'] \n \n block2a_se_reduce (Conv2D) (None, 1, 1, 4) 388 ['block2a_se_reshape[0][0]'] \n \n block2a_se_expand (Conv2D) (None, 1, 1, 96) 480 ['block2a_se_reduce[0][0]'] \n \n block2a_se_excite (Multiply) (None, 32, 32, 96) 0 ['block2a_activation[0][0]', \n 'block2a_se_expand[0][0]'] \n \n block2a_project_conv (Conv2D) (None, 32, 32, 24) 2304 ['block2a_se_excite[0][0]'] \n \n block2a_project_bn (BatchNorma (None, 32, 32, 24) 96 ['block2a_project_conv[0][0]'] \n lization) \n \n block2b_expand_conv (Conv2D) (None, 32, 32, 144) 3456 ['block2a_project_bn[0][0]'] \n \n block2b_expand_bn (BatchNormal (None, 32, 32, 144) 576 ['block2b_expand_conv[0][0]'] \n ization) \n \n block2b_expand_activation (Act (None, 32, 32, 144) 0 ['block2b_expand_bn[0][0]'] \n ivation) \n \n block2b_dwconv (DepthwiseConv2 (None, 32, 32, 144) 1296 ['block2b_expand_activation[0][0]\n D) '] \n \n block2b_bn (BatchNormalization (None, 32, 32, 144) 576 ['block2b_dwconv[0][0]'] \n ) \n \n block2b_activation (Activation (None, 32, 32, 144) 0 ['block2b_bn[0][0]'] \n ) \n \n block2b_se_squeeze (GlobalAver (None, 144) 0 ['block2b_activation[0][0]'] \n agePooling2D) \n \n block2b_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block2b_se_squeeze[0][0]'] \n \n block2b_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block2b_se_reshape[0][0]'] \n \n block2b_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block2b_se_reduce[0][0]'] \n \n block2b_se_excite (Multiply) (None, 32, 32, 144) 0 ['block2b_activation[0][0]', \n 'block2b_se_expand[0][0]'] \n \n block2b_project_conv (Conv2D) (None, 32, 32, 24) 3456 ['block2b_se_excite[0][0]'] \n \n block2b_project_bn (BatchNorma (None, 32, 32, 24) 96 ['block2b_project_conv[0][0]'] \n lization) \n \n block2b_drop (Dropout) (None, 32, 32, 24) 0 ['block2b_project_bn[0][0]'] \n \n block2b_add (Add) (None, 32, 32, 24) 0 ['block2b_drop[0][0]', \n 'block2a_project_bn[0][0]'] \n \n block3a_expand_conv (Conv2D) (None, 32, 32, 144) 3456 ['block2b_add[0][0]'] \n \n block3a_expand_bn (BatchNormal (None, 32, 32, 144) 576 ['block3a_expand_conv[0][0]'] \n ization) \n \n block3a_expand_activation (Act (None, 32, 32, 144) 0 ['block3a_expand_bn[0][0]'] \n ivation) \n \n block3a_dwconv_pad (ZeroPaddin (None, 35, 35, 144) 0 ['block3a_expand_activation[0][0]\n g2D) '] \n \n block3a_dwconv (DepthwiseConv2 (None, 16, 16, 144) 3600 ['block3a_dwconv_pad[0][0]'] \n D) \n \n block3a_bn (BatchNormalization (None, 16, 16, 144) 576 ['block3a_dwconv[0][0]'] \n ) \n \n block3a_activation (Activation (None, 16, 16, 144) 0 ['block3a_bn[0][0]'] \n ) \n \n block3a_se_squeeze (GlobalAver (None, 144) 0 ['block3a_activation[0][0]'] \n agePooling2D) \n \n block3a_se_reshape (Reshape) (None, 1, 1, 144) 0 ['block3a_se_squeeze[0][0]'] \n \n block3a_se_reduce (Conv2D) (None, 1, 1, 6) 870 ['block3a_se_reshape[0][0]'] \n \n block3a_se_expand (Conv2D) (None, 1, 1, 144) 1008 ['block3a_se_reduce[0][0]'] \n \n block3a_se_excite (Multiply) (None, 16, 16, 144) 0 ['block3a_activation[0][0]', \n 'block3a_se_expand[0][0]'] \n \n block3a_project_conv (Conv2D) (None, 16, 16, 40) 5760 ['block3a_se_excite[0][0]'] \n \n block3a_project_bn (BatchNorma (None, 16, 16, 40) 160 ['block3a_project_conv[0][0]'] \n lization) \n \n block3b_expand_conv (Conv2D) (None, 16, 16, 240) 9600 ['block3a_project_bn[0][0]'] \n \n block3b_expand_bn (BatchNormal (None, 16, 16, 240) 960 ['block3b_expand_conv[0][0]'] \n ization) \n \n block3b_expand_activation (Act (None, 16, 16, 240) 0 ['block3b_expand_bn[0][0]'] \n ivation) \n \n block3b_dwconv (DepthwiseConv2 (None, 16, 16, 240) 6000 ['block3b_expand_activation[0][0]\n D) '] \n \n block3b_bn (BatchNormalization (None, 16, 16, 240) 960 ['block3b_dwconv[0][0]'] \n ) \n \n block3b_activation (Activation (None, 16, 16, 240) 0 ['block3b_bn[0][0]'] \n ) \n \n block3b_se_squeeze (GlobalAver (None, 240) 0 ['block3b_activation[0][0]'] \n agePooling2D) \n \n block3b_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block3b_se_squeeze[0][0]'] \n \n block3b_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block3b_se_reshape[0][0]'] \n \n block3b_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block3b_se_reduce[0][0]'] \n \n block3b_se_excite (Multiply) (None, 16, 16, 240) 0 ['block3b_activation[0][0]', \n 'block3b_se_expand[0][0]'] \n \n block3b_project_conv (Conv2D) (None, 16, 16, 40) 9600 ['block3b_se_excite[0][0]'] \n \n block3b_project_bn (BatchNorma (None, 16, 16, 40) 160 ['block3b_project_conv[0][0]'] \n lization) \n \n block3b_drop (Dropout) (None, 16, 16, 40) 0 ['block3b_project_bn[0][0]'] \n \n block3b_add (Add) (None, 16, 16, 40) 0 ['block3b_drop[0][0]', \n 'block3a_project_bn[0][0]'] \n \n block4a_expand_conv (Conv2D) (None, 16, 16, 240) 9600 ['block3b_add[0][0]'] \n \n block4a_expand_bn (BatchNormal (None, 16, 16, 240) 960 ['block4a_expand_conv[0][0]'] \n ization) \n \n block4a_expand_activation (Act (None, 16, 16, 240) 0 ['block4a_expand_bn[0][0]'] \n ivation) \n \n block4a_dwconv_pad (ZeroPaddin (None, 17, 17, 240) 0 ['block4a_expand_activation[0][0]\n g2D) '] \n \n block4a_dwconv (DepthwiseConv2 (None, 8, 8, 240) 2160 ['block4a_dwconv_pad[0][0]'] \n D) \n \n block4a_bn (BatchNormalization (None, 8, 8, 240) 960 ['block4a_dwconv[0][0]'] \n ) \n \n block4a_activation (Activation (None, 8, 8, 240) 0 ['block4a_bn[0][0]'] \n ) \n \n block4a_se_squeeze (GlobalAver (None, 240) 0 ['block4a_activation[0][0]'] \n agePooling2D) \n \n block4a_se_reshape (Reshape) (None, 1, 1, 240) 0 ['block4a_se_squeeze[0][0]'] \n \n block4a_se_reduce (Conv2D) (None, 1, 1, 10) 2410 ['block4a_se_reshape[0][0]'] \n \n block4a_se_expand (Conv2D) (None, 1, 1, 240) 2640 ['block4a_se_reduce[0][0]'] \n \n block4a_se_excite (Multiply) (None, 8, 8, 240) 0 ['block4a_activation[0][0]', \n 'block4a_se_expand[0][0]'] \n \n block4a_project_conv (Conv2D) (None, 8, 8, 80) 19200 ['block4a_se_excite[0][0]'] \n \n block4a_project_bn (BatchNorma (None, 8, 8, 80) 320 ['block4a_project_conv[0][0]'] \n lization) \n \n block4b_expand_conv (Conv2D) (None, 8, 8, 480) 38400 ['block4a_project_bn[0][0]'] \n \n block4b_expand_bn (BatchNormal (None, 8, 8, 480) 1920 ['block4b_expand_conv[0][0]'] \n ization) \n \n block4b_expand_activation (Act (None, 8, 8, 480) 0 ['block4b_expand_bn[0][0]'] \n ivation) \n \n block4b_dwconv (DepthwiseConv2 (None, 8, 8, 480) 4320 ['block4b_expand_activation[0][0]\n D) '] \n \n block4b_bn (BatchNormalization (None, 8, 8, 480) 1920 ['block4b_dwconv[0][0]'] \n ) \n \n block4b_activation (Activation (None, 8, 8, 480) 0 ['block4b_bn[0][0]'] \n ) \n \n block4b_se_squeeze (GlobalAver (None, 480) 0 ['block4b_activation[0][0]'] \n agePooling2D) \n \n block4b_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4b_se_squeeze[0][0]'] \n \n block4b_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4b_se_reshape[0][0]'] \n \n block4b_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4b_se_reduce[0][0]'] \n \n block4b_se_excite (Multiply) (None, 8, 8, 480) 0 ['block4b_activation[0][0]', \n 'block4b_se_expand[0][0]'] \n \n block4b_project_conv (Conv2D) (None, 8, 8, 80) 38400 ['block4b_se_excite[0][0]'] \n \n block4b_project_bn (BatchNorma (None, 8, 8, 80) 320 ['block4b_project_conv[0][0]'] \n lization) \n \n block4b_drop (Dropout) (None, 8, 8, 80) 0 ['block4b_project_bn[0][0]'] \n \n block4b_add (Add) (None, 8, 8, 80) 0 ['block4b_drop[0][0]', \n 'block4a_project_bn[0][0]'] \n \n block4c_expand_conv (Conv2D) (None, 8, 8, 480) 38400 ['block4b_add[0][0]'] \n \n block4c_expand_bn (BatchNormal (None, 8, 8, 480) 1920 ['block4c_expand_conv[0][0]'] \n ization) \n \n block4c_expand_activation (Act (None, 8, 8, 480) 0 ['block4c_expand_bn[0][0]'] \n ivation) \n \n block4c_dwconv (DepthwiseConv2 (None, 8, 8, 480) 4320 ['block4c_expand_activation[0][0]\n D) '] \n \n block4c_bn (BatchNormalization (None, 8, 8, 480) 1920 ['block4c_dwconv[0][0]'] \n ) \n \n block4c_activation (Activation (None, 8, 8, 480) 0 ['block4c_bn[0][0]'] \n ) \n \n block4c_se_squeeze (GlobalAver (None, 480) 0 ['block4c_activation[0][0]'] \n agePooling2D) \n \n block4c_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block4c_se_squeeze[0][0]'] \n \n block4c_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block4c_se_reshape[0][0]'] \n \n block4c_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block4c_se_reduce[0][0]'] \n \n block4c_se_excite (Multiply) (None, 8, 8, 480) 0 ['block4c_activation[0][0]', \n 'block4c_se_expand[0][0]'] \n \n block4c_project_conv (Conv2D) (None, 8, 8, 80) 38400 ['block4c_se_excite[0][0]'] \n \n block4c_project_bn (BatchNorma (None, 8, 8, 80) 320 ['block4c_project_conv[0][0]'] \n lization) \n \n block4c_drop (Dropout) (None, 8, 8, 80) 0 ['block4c_project_bn[0][0]'] \n \n block4c_add (Add) (None, 8, 8, 80) 0 ['block4c_drop[0][0]', \n 'block4b_add[0][0]'] \n \n block5a_expand_conv (Conv2D) (None, 8, 8, 480) 38400 ['block4c_add[0][0]'] \n \n block5a_expand_bn (BatchNormal (None, 8, 8, 480) 1920 ['block5a_expand_conv[0][0]'] \n ization) \n \n block5a_expand_activation (Act (None, 8, 8, 480) 0 ['block5a_expand_bn[0][0]'] \n ivation) \n \n block5a_dwconv (DepthwiseConv2 (None, 8, 8, 480) 12000 ['block5a_expand_activation[0][0]\n D) '] \n \n block5a_bn (BatchNormalization (None, 8, 8, 480) 1920 ['block5a_dwconv[0][0]'] \n ) \n \n block5a_activation (Activation (None, 8, 8, 480) 0 ['block5a_bn[0][0]'] \n ) \n \n block5a_se_squeeze (GlobalAver (None, 480) 0 ['block5a_activation[0][0]'] \n agePooling2D) \n \n block5a_se_reshape (Reshape) (None, 1, 1, 480) 0 ['block5a_se_squeeze[0][0]'] \n \n block5a_se_reduce (Conv2D) (None, 1, 1, 20) 9620 ['block5a_se_reshape[0][0]'] \n \n block5a_se_expand (Conv2D) (None, 1, 1, 480) 10080 ['block5a_se_reduce[0][0]'] \n \n block5a_se_excite (Multiply) (None, 8, 8, 480) 0 ['block5a_activation[0][0]', \n 'block5a_se_expand[0][0]'] \n \n block5a_project_conv (Conv2D) (None, 8, 8, 112) 53760 ['block5a_se_excite[0][0]'] \n \n block5a_project_bn (BatchNorma (None, 8, 8, 112) 448 ['block5a_project_conv[0][0]'] \n lization) \n \n block5b_expand_conv (Conv2D) (None, 8, 8, 672) 75264 ['block5a_project_bn[0][0]'] \n \n block5b_expand_bn (BatchNormal (None, 8, 8, 672) 2688 ['block5b_expand_conv[0][0]'] \n ization) \n \n block5b_expand_activation (Act (None, 8, 8, 672) 0 ['block5b_expand_bn[0][0]'] \n ivation) \n \n block5b_dwconv (DepthwiseConv2 (None, 8, 8, 672) 16800 ['block5b_expand_activation[0][0]\n D) '] \n \n block5b_bn (BatchNormalization (None, 8, 8, 672) 2688 ['block5b_dwconv[0][0]'] \n ) \n \n block5b_activation (Activation (None, 8, 8, 672) 0 ['block5b_bn[0][0]'] \n ) \n \n block5b_se_squeeze (GlobalAver (None, 672) 0 ['block5b_activation[0][0]'] \n agePooling2D) \n \n block5b_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block5b_se_squeeze[0][0]'] \n \n block5b_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block5b_se_reshape[0][0]'] \n \n block5b_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block5b_se_reduce[0][0]'] \n \n block5b_se_excite (Multiply) (None, 8, 8, 672) 0 ['block5b_activation[0][0]', \n 'block5b_se_expand[0][0]'] \n \n block5b_project_conv (Conv2D) (None, 8, 8, 112) 75264 ['block5b_se_excite[0][0]'] \n \n block5b_project_bn (BatchNorma (None, 8, 8, 112) 448 ['block5b_project_conv[0][0]'] \n lization) \n \n block5b_drop (Dropout) (None, 8, 8, 112) 0 ['block5b_project_bn[0][0]'] \n \n block5b_add (Add) (None, 8, 8, 112) 0 ['block5b_drop[0][0]', \n 'block5a_project_bn[0][0]'] \n \n block5c_expand_conv (Conv2D) (None, 8, 8, 672) 75264 ['block5b_add[0][0]'] \n \n block5c_expand_bn (BatchNormal (None, 8, 8, 672) 2688 ['block5c_expand_conv[0][0]'] \n ization) \n \n block5c_expand_activation (Act (None, 8, 8, 672) 0 ['block5c_expand_bn[0][0]'] \n ivation) \n \n block5c_dwconv (DepthwiseConv2 (None, 8, 8, 672) 16800 ['block5c_expand_activation[0][0]\n D) '] \n \n block5c_bn (BatchNormalization (None, 8, 8, 672) 2688 ['block5c_dwconv[0][0]'] \n ) \n \n block5c_activation (Activation (None, 8, 8, 672) 0 ['block5c_bn[0][0]'] \n ) \n \n block5c_se_squeeze (GlobalAver (None, 672) 0 ['block5c_activation[0][0]'] \n agePooling2D) \n \n block5c_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block5c_se_squeeze[0][0]'] \n \n block5c_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block5c_se_reshape[0][0]'] \n \n block5c_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block5c_se_reduce[0][0]'] \n \n block5c_se_excite (Multiply) (None, 8, 8, 672) 0 ['block5c_activation[0][0]', \n 'block5c_se_expand[0][0]'] \n \n block5c_project_conv (Conv2D) (None, 8, 8, 112) 75264 ['block5c_se_excite[0][0]'] \n \n block5c_project_bn (BatchNorma (None, 8, 8, 112) 448 ['block5c_project_conv[0][0]'] \n lization) \n \n block5c_drop (Dropout) (None, 8, 8, 112) 0 ['block5c_project_bn[0][0]'] \n \n block5c_add (Add) (None, 8, 8, 112) 0 ['block5c_drop[0][0]', \n 'block5b_add[0][0]'] \n \n block6a_expand_conv (Conv2D) (None, 8, 8, 672) 75264 ['block5c_add[0][0]'] \n \n block6a_expand_bn (BatchNormal (None, 8, 8, 672) 2688 ['block6a_expand_conv[0][0]'] \n ization) \n \n block6a_expand_activation (Act (None, 8, 8, 672) 0 ['block6a_expand_bn[0][0]'] \n ivation) \n \n block6a_dwconv_pad (ZeroPaddin (None, 11, 11, 672) 0 ['block6a_expand_activation[0][0]\n g2D) '] \n \n block6a_dwconv (DepthwiseConv2 (None, 4, 4, 672) 16800 ['block6a_dwconv_pad[0][0]'] \n D) \n \n block6a_bn (BatchNormalization (None, 4, 4, 672) 2688 ['block6a_dwconv[0][0]'] \n ) \n \n block6a_activation (Activation (None, 4, 4, 672) 0 ['block6a_bn[0][0]'] \n ) \n \n block6a_se_squeeze (GlobalAver (None, 672) 0 ['block6a_activation[0][0]'] \n agePooling2D) \n \n block6a_se_reshape (Reshape) (None, 1, 1, 672) 0 ['block6a_se_squeeze[0][0]'] \n \n block6a_se_reduce (Conv2D) (None, 1, 1, 28) 18844 ['block6a_se_reshape[0][0]'] \n \n block6a_se_expand (Conv2D) (None, 1, 1, 672) 19488 ['block6a_se_reduce[0][0]'] \n \n block6a_se_excite (Multiply) (None, 4, 4, 672) 0 ['block6a_activation[0][0]', \n 'block6a_se_expand[0][0]'] \n \n block6a_project_conv (Conv2D) (None, 4, 4, 192) 129024 ['block6a_se_excite[0][0]'] \n \n block6a_project_bn (BatchNorma (None, 4, 4, 192) 768 ['block6a_project_conv[0][0]'] \n lization) \n \n block6b_expand_conv (Conv2D) (None, 4, 4, 1152) 221184 ['block6a_project_bn[0][0]'] \n \n block6b_expand_bn (BatchNormal (None, 4, 4, 1152) 4608 ['block6b_expand_conv[0][0]'] \n ization) \n \n block6b_expand_activation (Act (None, 4, 4, 1152) 0 ['block6b_expand_bn[0][0]'] \n ivation) \n \n block6b_dwconv (DepthwiseConv2 (None, 4, 4, 1152) 28800 ['block6b_expand_activation[0][0]\n D) '] \n \n block6b_bn (BatchNormalization (None, 4, 4, 1152) 4608 ['block6b_dwconv[0][0]'] \n ) \n \n block6b_activation (Activation (None, 4, 4, 1152) 0 ['block6b_bn[0][0]'] \n ) \n \n block6b_se_squeeze (GlobalAver (None, 1152) 0 ['block6b_activation[0][0]'] \n agePooling2D) \n \n block6b_se_reshape (Reshape) (None, 1, 1, 1152) 0 ['block6b_se_squeeze[0][0]'] \n \n block6b_se_reduce (Conv2D) (None, 1, 1, 48) 55344 ['block6b_se_reshape[0][0]'] \n \n block6b_se_expand (Conv2D) (None, 1, 1, 1152) 56448 ['block6b_se_reduce[0][0]'] \n \n block6b_se_excite (Multiply) (None, 4, 4, 1152) 0 ['block6b_activation[0][0]', \n 'block6b_se_expand[0][0]'] \n \n block6b_project_conv (Conv2D) (None, 4, 4, 192) 221184 ['block6b_se_excite[0][0]'] \n \n block6b_project_bn (BatchNorma (None, 4, 4, 192) 768 ['block6b_project_conv[0][0]'] \n lization) \n \n block6b_drop (Dropout) (None, 4, 4, 192) 0 ['block6b_project_bn[0][0]'] \n \n block6b_add (Add) (None, 4, 4, 192) 0 ['block6b_drop[0][0]', \n 'block6a_project_bn[0][0]'] \n \n block6c_expand_conv (Conv2D) (None, 4, 4, 1152) 221184 ['block6b_add[0][0]'] \n \n block6c_expand_bn (BatchNormal (None, 4, 4, 1152) 4608 ['block6c_expand_conv[0][0]'] \n ization) \n \n block6c_expand_activation (Act (None, 4, 4, 1152) 0 ['block6c_expand_bn[0][0]'] \n ivation) \n \n block6c_dwconv (DepthwiseConv2 (None, 4, 4, 1152) 28800 ['block6c_expand_activation[0][0]\n D) '] \n \n block6c_bn (BatchNormalization (None, 4, 4, 1152) 4608 ['block6c_dwconv[0][0]'] \n ) \n \n block6c_activation (Activation (None, 4, 4, 1152) 0 ['block6c_bn[0][0]'] \n ) \n \n block6c_se_squeeze (GlobalAver (None, 1152) 0 ['block6c_activation[0][0]'] \n agePooling2D) \n \n block6c_se_reshape (Reshape) (None, 1, 1, 1152) 0 ['block6c_se_squeeze[0][0]'] \n \n block6c_se_reduce (Conv2D) (None, 1, 1, 48) 55344 ['block6c_se_reshape[0][0]'] \n \n block6c_se_expand (Conv2D) (None, 1, 1, 1152) 56448 ['block6c_se_reduce[0][0]'] \n \n block6c_se_excite (Multiply) (None, 4, 4, 1152) 0 ['block6c_activation[0][0]', \n 'block6c_se_expand[0][0]'] \n \n block6c_project_conv (Conv2D) (None, 4, 4, 192) 221184 ['block6c_se_excite[0][0]'] \n \n block6c_project_bn (BatchNorma (None, 4, 4, 192) 768 ['block6c_project_conv[0][0]'] \n lization) \n \n block6c_drop (Dropout) (None, 4, 4, 192) 0 ['block6c_project_bn[0][0]'] \n \n block6c_add (Add) (None, 4, 4, 192) 0 ['block6c_drop[0][0]', \n 'block6b_add[0][0]'] \n \n block6d_expand_conv (Conv2D) (None, 4, 4, 1152) 221184 ['block6c_add[0][0]'] \n \n block6d_expand_bn (BatchNormal (None, 4, 4, 1152) 4608 ['block6d_expand_conv[0][0]'] \n ization) \n \n block6d_expand_activation (Act (None, 4, 4, 1152) 0 ['block6d_expand_bn[0][0]'] \n ivation) \n \n block6d_dwconv (DepthwiseConv2 (None, 4, 4, 1152) 28800 ['block6d_expand_activation[0][0]\n D) '] \n \n block6d_bn (BatchNormalization (None, 4, 4, 1152) 4608 ['block6d_dwconv[0][0]'] \n ) \n \n block6d_activation (Activation (None, 4, 4, 1152) 0 ['block6d_bn[0][0]'] \n ) \n \n block6d_se_squeeze (GlobalAver (None, 1152) 0 ['block6d_activation[0][0]'] \n agePooling2D) \n \n block6d_se_reshape (Reshape) (None, 1, 1, 1152) 0 ['block6d_se_squeeze[0][0]'] \n \n block6d_se_reduce (Conv2D) (None, 1, 1, 48) 55344 ['block6d_se_reshape[0][0]'] \n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow.keras.applications import ResNet50\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Dense, Flatten, Dropout\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.utils import to_categorical\nimport joblib\nfrom sklearn.model_selection import train_test_split\nfrom PIL import Image\nimport os\n\n# ✅ Load dataset\ntrain_data = pd.read_csv('/kaggle/input/UBC-OCEAN/train.csv')\n\n# ✅ Check dataset structure\nprint(\"Dataset Loaded Successfully!\")\nprint(\"Columns in Train Data:\", train_data.columns)\n\n# ✅ Encode labels numerically\nclass_labels = {'CC': 0, 'EC': 1, 'HGSC': 2, 'LGSC': 3, 'MC': 4}\ntrain_data[\"label\"] = train_data[\"label\"].map(class_labels)\n\n# ✅ Splitting dataset dynamically (70% Train, 15% Validation, 15% Test)\ntrain_set, test_set = train_test_split(train_data, test_size=0.15, stratify=train_data[\"label\"], random_state=42)\ntrain_set, val_set = train_test_split(train_set, test_size=0.15, stratify=train_set[\"label\"], random_state=42)\n\nprint(f\"Training Data: {len(train_set)} images\")\nprint(f\"Validation Data: {len(val_set)} images\")\nprint(f\"Testing Data: {len(test_set)} images\")\n\n# ✅ Image Preprocessing\ndef load_and_preprocess_images(df, img_folder):\n images = []\n labels = []\n \n for index, row in df.iterrows():\n img_id, label = row[\"image_id\"], row[\"label\"]\n img_path = os.path.join(img_folder, f\"{img_id}.png\")\n \n try:\n img = Image.open(img_path).resize((128, 128)) # Resize for ResNet50 input\n img_array = np.array(img) / 255.0 # Normalize\n images.append(img_array)\n labels.append(label)\n except Exception as e:\n print(f\"Error loading {img_path}: {e}\")\n\n return np.array(images), np.array(labels)\n\n# ✅ Load images (update correct paths)\ntrain_images, train_labels = load_and_preprocess_images(train_set, \"/kaggle/input/UBC-OCEAN/train_images\")\nval_images, val_labels = load_and_preprocess_images(val_set, \"/kaggle/input/UBC-OCEAN/train_images\")\ntest_images, test_labels = load_and_preprocess_images(test_set, \"/kaggle/input/UBC-OCEAN/train_images\")\n\n# ✅ Convert labels to categorical (one-hot encoding)\ntrain_labels = to_categorical(train_labels, num_classes=5)\nval_labels = to_categorical(val_labels, num_classes=5)\ntest_labels = to_categorical(test_labels, num_classes=5)\n\n# ✅ Define ResNet50 Model\nbase_model = ResNet50(weights='imagenet', include_top=False, input_shape=(128, 128, 3))\n\n# ✅ Freeze Base Model Layers\nfor layer in base_model.layers:\n layer.trainable = False\n\n# ✅ Custom Classification Head\nx = Flatten()(base_model.output)\nx = Dense(512, activation='relu')(x)\nx = Dropout(0.5)(x)\nx = Dense(256, activation='relu')(x)\nx = Dropout(0.3)(x)\npredictions = Dense(5, activation='softmax')(x) # 5 Classes for Ovarian Cancer\n\n# ✅ Final Model\nmodel = Model(inputs=base_model.input, outputs=predictions)\n\n# ✅ Compile Model\nmodel.compile(optimizer=Adam(learning_rate=0.0001),\n loss='categorical_crossentropy',\n metrics=['accuracy'])\n\n# ✅ Summary\nmodel.summary()\n\n# ✅ Train Model\nhistory = model.fit(train_images, train_labels, \n epochs=15, batch_size=32, \n validation_data=(val_images, val_labels))\n\n# ✅ Save Trained Model\nmodel.save(\"ResNet50_OVC_Model.h5\")\n\n# ✅ Evaluate Model on Test Data\ntest_loss, test_acc = model.evaluate(test_images, test_labels, verbose=0)\nprint(f\"Test Accuracy: {test_acc * 100:.2f}%\")\n\n# ✅ Confusion Matrix and Classification Report\nfrom sklearn.metrics import confusion_matrix, classification_report\n\ny_pred_prob = model.predict(test_images)\ny_pred_test = np.argmax(y_pred_prob, axis=1)\ny_true_test = np.argmax(test_labels, axis=1)\n\nprint(\"Confusion Matrix:\\n\", confusion_matrix(y_true_test, y_pred_test))\nprint(\"\\nClassification Report:\\n\", classification_report(y_true_test, y_pred_test))\n\n# ✅ Save Predictions for Future Use\njoblib.dump(y_pred_test, \"y_pred_test.joblib\")\n\n\nimport pandas as pd\n\n# Load dataset\ntrain_data = pd.read_csv('/kaggle/input/UBC-OCEAN/train.csv')\ntest_data = pd.read_csv('/kaggle/input/UBC-OCEAN/test.csv')\n\n# Display available columns\nprint(\"Columns in Train Data:\", train_data.columns)\nprint(\"Columns in Test Data:\", test_data.columns)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-05T07:16:01.58211Z","iopub.execute_input":"2025-03-05T07:16:01.582553Z","iopub.status.idle":"2025-03-05T07:17:03.756509Z","shell.execute_reply.started":"2025-03-05T07:16:01.582524Z","shell.execute_reply":"2025-03-05T07:17:03.755279Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\n\n# CSV file load karne ka sahi path use karte hue\ndata = pd.read_csv('/kaggle/input/UBC-OCEAN/train.csv')\n\n# Check karein agar dataset sahi load hua hai\nprint(\"Dataset Loaded Successfully!\")\nprint(\"Total Images in Dataset:\", len(data))\nprint(\"Columns Available:\", data.columns)\n\n# WSI aur TMA images ko alag-alag filter karna\nwsi_total = data[data[\"is_tma\"] == False] # WSI images\ntma_total = data[data[\"is_tma\"] == True] # TMA images\n\nprint(f\"Total WSI Images: {len(wsi_total)}\")\nprint(f\"Total TMA Images: {len(tma_total)}\")\n\n# Splitting ratios define karna: Training = 70%, Validation = 15%, Testing = 15%\ntrain_ratio = 0.7 \nval_ratio = 0.15 \ntest_ratio = 0.15 \n\n# WSI images ko shuffle karke split karna\nwsi_train, wsi_val, wsi_test = np.split(wsi_total.sample(frac=1, random_state=42), \n [int(train_ratio * len(wsi_total)), int((train_ratio + val_ratio) * len(wsi_total))])\n\n# TMA images ko shuffle karke split karna\ntma_train, tma_val, tma_test = np.split(tma_total.sample(frac=1, random_state=42), \n [int(train_ratio * len(tma_total)), int((train_ratio + val_ratio) * len(tma_total))])\n\n# WSI aur TMA splits ko combine karna final sets ke liye\ntrain_data = pd.concat([wsi_train, tma_train])\nval_data = pd.concat([wsi_val, tma_val])\ntest_data = pd.concat([wsi_test, tma_test])\n\n# Final counts print karna\nprint(f\"Training Set: {len(train_data)} images\")\nprint(f\"Validation Set: {len(val_data)} images\")\nprint(f\"Testing Set: {len(test_data)} images\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-05T06:59:47.964107Z","iopub.execute_input":"2025-03-05T06:59:47.964903Z","iopub.status.idle":"2025-03-05T06:59:48.011148Z","shell.execute_reply.started":"2025-03-05T06:59:47.964869Z","shell.execute_reply":"2025-03-05T06:59:48.010033Z"}},"outputs":[{"name":"stdout","text":"Dataset Loaded Successfully!\nTotal Images in Dataset: 538\nColumns Available: Index(['image_id', 'label', 'image_width', 'image_height', 'is_tma'], dtype='object')\nTotal WSI Images: 513\nTotal TMA Images: 25\nTraining Set: 376 images\nValidation Set: 81 images\nTesting Set: 81 images\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"# 1st\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nfrom statsmodels.tools.sm_exceptions import ConvergenceWarning\nwarnings.simplefilter(\"ignore\", ConvergenceWarning)\n\nimport pandas as pd\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt\nfrom matplotlib.image import imread\n%matplotlib inline\nimport seaborn as sns\n\nfrom sklearn.metrics import classification_report , confusion_matrix , accuracy_score , auc\nfrom sklearn.model_selection import train_test_split\n\nimport cv2\n#from google.colab.patches import cv2_imshow\nfrom PIL import Image \nimport tensorflow as tf\nfrom tensorflow import keras\nfrom keras import Sequential\nfrom keras.layers import Input, Dense,Conv2D , MaxPooling2D, Flatten,BatchNormalization,Dropout\nfrom tensorflow.keras.preprocessing import image_dataset_from_directory\nimport tensorflow_hub as hub \n\nfrom keras.applications.vgg19 import VGG19\n\nimport joblib\n# 2nd \nlearn = pd.read_csv('/kaggle/input/UBC-OCEAN/train.csv')\ndata = learn.copy()\n# 3rd\ndata.shape\n# 4th\nmodel1_train_data = data[data[\"is_tma\"]==False]\nmodel1_train_data.head()\n# 5th\nclass_labels = ['CC', 'EC', 'HGSC', 'LGSC', 'MC']\nmodel1_train_data['label'] = model1_train_data['label'].replace({'CC':0, 'EC':1, 'HGSC':2, 'LGSC':3, 'MC':4})\nmodel1_train_data.head()\n# 6th\nmodel1_test_data = data[data[\"is_tma\"]==True]\nmodel1_test_data.head()\n# 7th\nclass_labels = ['CC', 'EC', 'HGSC', 'LGSC', 'MC']\nmodel1_test_data['label'] = model1_test_data['label'].replace({'CC':0, 'EC':1, 'HGSC':2, 'LGSC':3, 'MC':4})\nmodel1_test_data.head()\n# 8th\ntma_total_count = len(data[data[\"is_tma\"]==True])\ntma_total_count\n# 9th\nmodel1_test_data[0:12]\n# 10th\nwsi_total_count = len(data[data[\"is_tma\"]==False])\nwsi_total_count\n# 11th\nmodel1_train_data[0:50]\n# 12th\nmodel1_train_data = model1_train_data[0:50] # Only first 50 elements will be used to train Model 1\nmodel1_test_data = model1_test_data[0:12] # Only first 12 of 25 TMAs available are used to test the model\n# 13th\nImage.MAX_IMAGE_PIXELS = 10000000000\n# Define patch size and overlap (if needed)\npatch_size = (128,128) # Adjust this according to your requirements\noverlap = 0 # Adjust this if you want overlapping patches\n\nimage_data = []\nimage_label = []\nempty_img=0\nfor img_id, label , tma in zip(model1_train_data['image_id'],model1_train_data['label'], model1_train_data['is_tma']):\n #print(img_id, label, tma)\n if tma==0:\n img_name = str(img_id)+\"_thumbnail.png\"\n large_image = Image.open(\"/kaggle/input/UBC-OCEAN/train_thumbnails/\"+img_name)\n for y in range(0, large_image.height, patch_size[0] - overlap): # (0,2523,192)\n for x in range(0, large_image.width, patch_size[1] - overlap): # (0,3000,192) 224-32=192\n patch = large_image.crop((x, y, x+patch_size[1], y+patch_size[0]))\n image = np.array(patch)\n if np.sum(image)==0:\n empty_img+=1\n elif (np.sum(image[0:,0:50])==0) or (np.sum(image[0:,50:])==0) or (np.sum(image[0:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:])==0) or (np.sum(image[50:,0:])==0) or (np.sum(image[100:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:50])==0) or (np.sum(image[0:50,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:100,0:50])==0) or (np.sum(image[50:100,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:,0:100])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,75:])==0) or (np.sum(image[50:,75:])==0):\n empty_img+=1\n elif (np.sum(image[0:40,80:])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:35,0:])==0) or (np.sum(image[80:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:50])==0) or (np.sum(image[0:25,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:25])==0) or (np.sum(image[0:20,25:50])==0) or (np.sum(image[0:20,50:80])==0) or (np.sum(image[0:20,90:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:25])==0) or (np.sum(image[0:25,100:])==0) or (np.sum(image[100:,0:25])==0) or (np.sum(image[100:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:15,0:])==0) or (np.sum(image[115:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:,0:15])==0) or (np.sum(image[0:,115:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:20])==0) or (np.sum(image[0:20,110:])==0) or (np.sum(image[110:,0:20])==0) or (np.sum(image[110:,110:])==0):\n empty_img+=1\n elif (np.sum(image[0:10,0:10])==0) or (np.sum(image[0:10,115:])==0) or (np.sum(image[0:10,40:60])==0) or (np.sum(image[0:10,80:100])==0):\n empty_img+=1\n elif (np.sum(image[40:50,0:10])==0) or (np.sum(image[110:,80:100])==0) or (np.sum(image[70:85,70:90])==0) or (np.sum(image[50:60,110:])==0):\n empty_img+=1\n\n else:\n image_data.append(image)\n image_label.append(label)\n \n elif tma==1:\n img_name = str(img_id)+\".png\"\n large_image = Image.open(\"/kaggle/input/UBC-OCEAN/train_images/\"+img_name)\n for y in range(0, large_image.height, patch_size[0] - overlap): # (0,2523,192)\n for x in range(0, large_image.width, patch_size[1] - overlap): # (0,3000,192) 224-32=192\n patch = large_image.crop((x, y, x+patch_size[1], y+patch_size[0]))\n image = np.array(patch)\n if np.sum(image)==0:\n empty_img+=1\n elif (np.sum(image[0:,0:50])==0) or (np.sum(image[0:,50:])==0) or (np.sum(image[0:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:])==0) or (np.sum(image[50:,0:])==0) or (np.sum(image[100:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:50])==0) or (np.sum(image[0:50,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:100,0:50])==0) or (np.sum(image[50:100,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:,0:100])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,75:])==0) or (np.sum(image[50:,75:])==0):\n empty_img+=1\n elif (np.sum(image[0:40,80:])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:35,0:])==0) or (np.sum(image[80:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:50])==0) or (np.sum(image[0:25,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:25])==0) or (np.sum(image[0:20,25:50])==0) or (np.sum(image[0:20,50:80])==0) or (np.sum(image[0:20,90:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:25])==0) or (np.sum(image[0:25,100:])==0) or (np.sum(image[100:,0:25])==0) or (np.sum(image[100:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:15,0:])==0) or (np.sum(image[115:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:,0:15])==0) or (np.sum(image[0:,115:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:20])==0) or (np.sum(image[0:20,110:])==0) or (np.sum(image[110:,0:20])==0) or (np.sum(image[110:,110:])==0):\n empty_img+=1\n elif (np.sum(image[0:10,0:10])==0) or (np.sum(image[0:10,115:])==0) or (np.sum(image[0:10,40:60])==0) or (np.sum(image[0:10,80:100])==0):\n empty_img+=1\n elif (np.sum(image[40:50,0:10])==0) or (np.sum(image[110:,80:100])==0) or (np.sum(image[70:85,70:90])==0) or (np.sum(image[50:60,110:])==0):\n empty_img+=1\n \n \n else:\n image_data.append(image)\n image_label.append(label)\n# 14th\nmodel1_train_image_data = image_data\nprint(len(model1_train_image_data))\n\nmodel1_train_image_label = image_label\nprint(len(model1_train_image_label))\n\nprint(empty_img)\n# 15\nmodel1_train_image_data[0].shape\n# 16\nmodel1_train_image_data[0]\n# 17\nx_train = np.array(model1_train_image_data) \ny_train = np.array(model1_train_image_label)\n# 18\nprint(x_train.shape)\nprint(y_train.shape)\n# 19\n# Save the NumPy array to a file\n# joblib.dump(x3, 'x_24000.joblib')\n\njoblib.dump(x_train, 'x_data_train.joblib')\njoblib.dump(y_train, 'y_data_train.joblib')\n# 20\nx_data_train = joblib.load(\"/kaggle/working/x_data_train.joblib\")\ny_data_train = joblib.load(\"/kaggle/working/y_data_train.joblib\")\nprint(x_data_train.shape)\nprint(y_data_train.shape)\n# 21\nmodel1_test_data = data[data[\"is_tma\"]==True]\nmodel1_test_data.head()\n# 22\nclass_labels = ['CC', 'EC', 'HGSC', 'LGSC', 'MC']\nmodel1_test_data['label'] = model1_test_data['label'].replace({'CC':0, 'EC':1, 'HGSC':2, 'LGSC':3, 'MC':4})\nmodel1_test_data.head()\n# 23\nImage.MAX_IMAGE_PIXELS = 10000000000\n# Define patch size and overlap (if needed)\npatch_size = (128,128) # Adjust this according to your requirements\noverlap = 0 # Adjust this if you want overlapping patches\n\nimage_data = []\nimage_label = []\nempty_img=0\nfor img_id, label , tma in zip(model1_test_data['image_id'],model1_test_data['label'], model1_test_data['is_tma']):\n #print(img_id, label, tma)\n if tma==0:\n img_name = str(img_id)+\"_thumbnail.png\"\n large_image = Image.open(\"/kaggle/input/UBC-OCEAN/train_thumbnails/\"+img_name)\n for y in range(0, large_image.height, patch_size[0] - overlap): # (0,2523,192)\n for x in range(0, large_image.width, patch_size[1] - overlap): # (0,3000,192) 224-32=192\n patch = large_image.crop((x, y, x+patch_size[1], y+patch_size[0]))\n image = np.array(patch)\n if np.sum(image)==0:\n empty_img+=1\n elif (np.sum(image[0:,0:50])==0) or (np.sum(image[0:,50:])==0) or (np.sum(image[0:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:])==0) or (np.sum(image[50:,0:])==0) or (np.sum(image[100:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:50])==0) or (np.sum(image[0:50,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:100,0:50])==0) or (np.sum(image[50:100,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:,0:100])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,75:])==0) or (np.sum(image[50:,75:])==0):\n empty_img+=1\n elif (np.sum(image[0:40,80:])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:35,0:])==0) or (np.sum(image[80:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:50])==0) or (np.sum(image[0:25,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:25])==0) or (np.sum(image[0:20,25:50])==0) or (np.sum(image[0:20,50:80])==0) or (np.sum(image[0:20,90:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:25])==0) or (np.sum(image[0:25,100:])==0) or (np.sum(image[100:,0:25])==0) or (np.sum(image[100:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:15,0:])==0) or (np.sum(image[115:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:,0:15])==0) or (np.sum(image[0:,115:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:20])==0) or (np.sum(image[0:20,110:])==0) or (np.sum(image[110:,0:20])==0) or (np.sum(image[110:,110:])==0):\n empty_img+=1\n elif (np.sum(image[0:10,0:10])==0) or (np.sum(image[0:10,115:])==0) or (np.sum(image[0:10,40:60])==0) or (np.sum(image[0:10,80:100])==0):\n empty_img+=1\n elif (np.sum(image[40:50,0:10])==0) or (np.sum(image[110:,80:100])==0) or (np.sum(image[70:85,70:90])==0) or (np.sum(image[50:60,110:])==0):\n empty_img+=1\n\n else:\n image_data.append(image)\n image_label.append(label)\n \n elif tma==1:\n img_name = str(img_id)+\".png\"\n large_image = Image.open(\"/kaggle/input/UBC-OCEAN/train_images/\"+img_name)\n for y in range(0, large_image.height, patch_size[0] - overlap): # (0,2523,192)\n for x in range(0, large_image.width, patch_size[1] - overlap): # (0,3000,192) 224-32=192\n patch = large_image.crop((x, y, x+patch_size[1], y+patch_size[0]))\n image = np.array(patch)\n if np.sum(image)==0:\n empty_img+=1\n elif (np.sum(image[0:,0:50])==0) or (np.sum(image[0:,50:])==0) or (np.sum(image[0:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:])==0) or (np.sum(image[50:,0:])==0) or (np.sum(image[100:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:50])==0) or (np.sum(image[0:50,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:100,0:50])==0) or (np.sum(image[50:100,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:,0:100])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,75:])==0) or (np.sum(image[50:,75:])==0):\n empty_img+=1\n elif (np.sum(image[0:40,80:])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:35,0:])==0) or (np.sum(image[80:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:50])==0) or (np.sum(image[0:25,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:25])==0) or (np.sum(image[0:20,25:50])==0) or (np.sum(image[0:20,50:80])==0) or (np.sum(image[0:20,90:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:25])==0) or (np.sum(image[0:25,100:])==0) or (np.sum(image[100:,0:25])==0) or (np.sum(image[100:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:15,0:])==0) or (np.sum(image[115:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:,0:15])==0) or (np.sum(image[0:,115:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:20])==0) or (np.sum(image[0:20,110:])==0) or (np.sum(image[110:,0:20])==0) or (np.sum(image[110:,110:])==0):\n empty_img+=1\n elif (np.sum(image[0:10,0:10])==0) or (np.sum(image[0:10,115:])==0) or (np.sum(image[0:10,40:60])==0) or (np.sum(image[0:10,80:100])==0):\n empty_img+=1\n elif (np.sum(image[40:50,0:10])==0) or (np.sum(image[110:,80:100])==0) or (np.sum(image[70:85,70:90])==0) or (np.sum(image[50:60,110:])==0):\n empty_img+=1\n \n \n else:\n image_data.append(image)\n image_label.append(label)\n \n\n# 24\nmodel1_test_image_data = image_data\nprint(len(model1_test_image_data))\n\nmodel1_test_image_label = image_label\nprint(len(model1_test_image_label))\n\nprint(empty_img)\n# 25\nmodel1_test_image_data[0].shape\n# 26\nmodel1_test_image_data[0]\n# 27\n# converting variables on numoy arrays.\nx_test = np.array(model1_test_image_data) \ny_test = np.array(model1_test_image_label)\n# 28\n# checking variables shape\nprint(x_test.shape)\nprint(y_test.shape)\n# 29\n# exporting variables as job.lib\njoblib.dump(x_test, 'x_data_test.joblib')\njoblib.dump(y_test, 'y_data_test.joblib')\n# 30\n# loading .joblib file on working folder\nx_data_test = joblib.load(\"/kaggle/working/x_data_test.joblib\")\ny_data_test = joblib.load(\"/kaggle/working/y_data_test.joblib\")\nprint(x_data_test.shape)\nprint(y_data_test.shape)\n# 31\n# matching independent and target variables of training and testing datasets for model 1\nx_train = x_data_train \nx_test = x_data_test\ny_train = y_data_train\ny_test = y_data_test\n# 32\n# Scaling the data for train images\nx_train_scaled = x_train/255\nx_test_scaled = x_test/255\n# 33\n# Model building using VGG16 Model available on Keras\n\nfrom tensorflow.keras.applications import VGG16\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Dense, Flatten\nfrom tensorflow.keras.optimizers import Adam\nnum_classes = 5\n# Load the VGG16 model with ImageNet weights and exclude the top classification layer\nbase_model = VGG16(weights='imagenet', include_top=False, input_shape=(128,128, 3))\n\n# Customize the top classification layers\nx = base_model.output\nx = Flatten()(x)\nx = Dense(1000, activation='relu')(x) #original is 4096\nx = Dense(1000, activation='relu')(x) #original is 4096\npredictions = Dense(num_classes, activation='softmax')(x) # Replace num_classes with the number of classes in your dataset\n\n# Create the VGG16 model with your custom top layer\nmodel = Model(inputs=base_model.input, outputs=predictions)\n\n# Freeze pre-trained layers (optional)\nfor layer in base_model.layers:\n layer.trainable = False\n\nmodel.summary()\n# 34\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.datasets import mnist\nfrom tensorflow.keras.utils import to_categorical\n\n# Load and preprocess the dataset (e.g., MNIST dataset)\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\n# Reshape the data to include channel dimension and normalize\nx_train_scaled = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.0\nx_test_scaled = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.0\n\n# Initialize a simple model (e.g., CNN for image classification)\nmodel = Sequential([\n Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n MaxPooling2D(pool_size=(2, 2)),\n Conv2D(64, (3, 3), activation='relu'),\n MaxPooling2D(pool_size=(2, 2)),\n Flatten(),\n Dense(128, activation='relu'),\n Dense(10, activation='softmax')\n])\n\n# Compile the model with optimizer, loss, and metric\nmodel.compile(optimizer=Adam(lr=0.0001),\n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\n# Train the model\nhistory = model.fit(x_train_scaled, y_train, \n epochs=15, \n batch_size=64, \n validation_data=(x_test_scaled, y_test))\n\n# Evaluate the model on the test set\ntest_loss, test_accuracy = model.evaluate(x_test_scaled, y_test)\nprint(f'Test accuracy: {test_accuracy:.4f}')\n# 35\n# Evaluate the model on training data\ntrain_loss, train_acc = model.evaluate(x_train_scaled, y_train, verbose=0)\nprint(f\"Accuracy on Train Data: {train_acc * 100:.2f}%\")\n\n# Evaluate the model on test data\ntest_loss, test_acc = model.evaluate(x_test_scaled, y_test, verbose=0)\nprint(f\"Accuracy on Test Data: {test_acc * 100:.2f}%\")\n# 36\ny_pred = model.predict(x_test_scaled)\ny_pred_test = [np.argmax(i) for i in y_pred]\n# 37\nlen(y_pred_test)\n# 38\n# Save the model to a file\nmodel.save(\"UBC-OCEAN-CHL1-model1.h5\")\n# 39\n# Metrics evaluation on test data\nprint(\"Confusion Matrix:\\n\",confusion_matrix(y_test,y_pred_test))\nprint()\nprint(\"Classification Report:\\n\",classification_report(y_test,y_pred_test))\n# 40\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import roc_curve, auc\nfrom tensorflow.keras.models import load_model\nfrom sklearn.preprocessing import label_binarize\n\n# Load your saved model\nmodel = load_model('UBC-OCEAN-CHL1-model1.h5')\n\n# Predict probabilities (for multi-class classification)\ny_pred_prob = model.predict(x_test_scaled)\n\n# Binarize the output labels for multi-class ROC AUC\ny_test_binarized = label_binarize(y_test, classes=[0, 1, 2, 3, 4])\n\n# Compute ROC curve and ROC area for each class\nfpr = dict()\ntpr = dict()\nroc_auc = dict()\nn_classes = y_test_binarized.shape[1]\n\nfor i in range(n_classes):\n fpr[i], tpr[i], _ = roc_curve(y_test_binarized[:, i], y_pred_prob[:, i])\n roc_auc[i] = auc(fpr[i], tpr[i])\n\n# Plot ROC curves for each class\nplt.figure()\nfor i in range(n_classes):\n plt.plot(fpr[i], tpr[i], label=f'Class {i} (area = {roc_auc[i]:.2f})')\n\nplt.plot([0, 1], [0, 1], 'k--', lw=2) # Diagonal line\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('ROC Curve for Multi-class Classification')\nplt.legend(loc='lower right')\nplt.show()\n# 42\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix\n\n# Assuming y_test contains the true labels and y_pred_test contains the predicted labels\nconf_matrix = confusion_matrix(y_test, y_pred_test)\n\n# Plot confusion matrix\nplt.figure(figsize=(8, 6))\nsns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False,\n xticklabels=class_labels, yticklabels=class_labels)\nplt.xlabel('Predicted Labels')\nplt.ylabel('True Labels')\nplt.title('Confusion Matrix')\nplt.show()\n# 43\nimport matplotlib.pyplot as plt\n\n# Assuming 'history' is the result of the model.fit() function\n# 'history.history['accuracy']' contains training accuracy\n# 'history.history['val_accuracy']' contains validation accuracy\n\n# Plot training & validation accuracy values\nplt.figure(figsize=(8, 6))\nplt.plot(history.history['accuracy'], label='Training Accuracy')\nplt.plot(history.history['val_accuracy'], label='Validation Accuracy')\nplt.title('Training and Validation Accuracy')\nplt.xlabel('Epochs')\nplt.ylabel('Accuracy')\nplt.legend(loc='lower right')\nplt.show()\n# 44\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import roc_curve, auc\nfrom tensorflow.keras.models import load_model\nfrom sklearn.preprocessing import label_binarize\n\n# Load your saved model (already trained)\nmodel = load_model('UBC-OCEAN-CHL1-model1.h5')\n\n# Predict probabilities on the test dataset (for multi-class classification)\ny_pred_prob = model.predict(x_test_scaled)\n\n# Binarize the test labels for multi-class ROC AUC\ny_test_binarized = label_binarize(y_test, classes=[0, 1, 2, 3, 4])\n\n# Compute ROC curve and ROC area for each class\nfpr = dict()\ntpr = dict()\nroc_auc = dict()\nn_classes = y_test_binarized.shape[1]\n\nfor i in range(n_classes):\n fpr[i], tpr[i], _ = roc_curve(y_test_binarized[:, i], y_pred_prob[:, i])\n roc_auc[i] = auc(fpr[i], tpr[i])\n\n# Plot ROC curves for each class\nplt.figure(figsize=(10, 8))\nfor i in range(n_classes):\n plt.plot(fpr[i], tpr[i], lw=2, label=f'Class {class_labels[i]} (area = {roc_auc[i]:.2f})')\n\nplt.plot([0, 1], [0, 1], 'k--', lw=2) # Diagonal line (random classifier)\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('ROC Curve for Multi-class Classification')\nplt.legend(loc='lower right')\nplt.show()\n#44a\nimport matplotlib.pyplot as plt\n\n# Plot training & validation loss values\nplt.figure(figsize=(8, 6))\nplt.plot(history.history['loss'], label='Training Loss')\nplt.plot(history.history['val_loss'], label='Validation Loss')\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.title('Training and Validation Loss')\nplt.legend()\nplt.show()\n#44aa\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef smooth_curve(points, factor=0.8):\n \"\"\"Smooth the loss curve for better visualization.\"\"\"\n smoothed_points = []\n for point in points:\n if smoothed_points:\n smoothed_points.append(smoothed_points[-1] * factor + point * (1 - factor))\n else:\n smoothed_points.append(point)\n return smoothed_points\n\n# Plot smoothed loss values\nplt.figure(figsize=(8, 6))\nplt.plot(smooth_curve(history.history['loss']), label='Smoothed Training Loss', color='blue')\nplt.plot(smooth_curve(history.history['val_loss']), label='Smoothed Validation Loss', color='red')\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.title('Training and Validation Loss (Smoothed)')\nplt.legend()\nplt.show()\n\n\n\n\n# 45\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix\n\n# Assuming y_test contains the true labels and y_pred_test contains the predicted labels\nconf_matrix = confusion_matrix(y_test, y_pred_test)\n\n# Plot confusion matrix with class labels\nplt.figure(figsize=(8, 6))\nsns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False,\n xticklabels=class_labels, yticklabels=class_labels)\n\n# Add labels and title\nplt.xlabel('Predicted Labels')\nplt.ylabel('True Labels')\nplt.title('Confusion Matrix - Classwise')\nplt.show()\n# 46\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix, accuracy_score\n\n# Assuming y_test contains the true labels and y_pred_test contains the predicted labels\nconf_matrix = confusion_matrix(y_test, y_pred_test)\n\n# Calculate overall accuracy\noverall_accuracy = accuracy_score(y_test, y_pred_test)\nprint(f\"Overall Accuracy: {overall_accuracy * 100:.2f}%\")\n\n# Plot confusion matrix with class labels\nplt.figure(figsize=(8, 6))\nsns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False,\n xticklabels=class_labels, yticklabels=class_labels)\n\n# Add labels and title\nplt.xlabel('Predicted Labels')\nplt.ylabel('True Labels')\nplt.title('Confusion Matrix - Classwise')\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-03-04T05:03:06.102831Z","iopub.execute_input":"2025-03-04T05:03:06.103164Z","iopub.status.idle":"2025-03-04T05:11:07.957453Z","shell.execute_reply.started":"2025-03-04T05:03:06.103139Z","shell.execute_reply":"2025-03-04T05:11:07.956314Z"}},"outputs":[{"name":"stdout","text":"8879\n8879\n10225\n(8879, 128, 128, 3)\n(8879,)\n(8879, 128, 128, 3)\n(8879,)\n15283\n15283\n1259\n(15283, 128, 128, 3)\n(15283,)\n(15283, 128, 128, 3)\n(15283,)\nDownloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n58889256/58889256 [==============================] - 3s 0us/step\nModel: \"model\"\n_________________________________________________________________\n Layer (type) Output Shape Param # \n=================================================================\n input_1 (InputLayer) [(None, 128, 128, 3)] 0 \n \n block1_conv1 (Conv2D) (None, 128, 128, 64) 1792 \n \n block1_conv2 (Conv2D) (None, 128, 128, 64) 36928 \n \n block1_pool (MaxPooling2D) (None, 64, 64, 64) 0 \n \n block2_conv1 (Conv2D) (None, 64, 64, 128) 73856 \n \n block2_conv2 (Conv2D) (None, 64, 64, 128) 147584 \n \n block2_pool (MaxPooling2D) (None, 32, 32, 128) 0 \n \n block3_conv1 (Conv2D) (None, 32, 32, 256) 295168 \n \n block3_conv2 (Conv2D) (None, 32, 32, 256) 590080 \n \n block3_conv3 (Conv2D) (None, 32, 32, 256) 590080 \n \n block3_pool (MaxPooling2D) (None, 16, 16, 256) 0 \n \n block4_conv1 (Conv2D) (None, 16, 16, 512) 1180160 \n \n block4_conv2 (Conv2D) (None, 16, 16, 512) 2359808 \n \n block4_conv3 (Conv2D) (None, 16, 16, 512) 2359808 \n \n block4_pool (MaxPooling2D) (None, 8, 8, 512) 0 \n \n block5_conv1 (Conv2D) (None, 8, 8, 512) 2359808 \n \n block5_conv2 (Conv2D) (None, 8, 8, 512) 2359808 \n \n block5_conv3 (Conv2D) (None, 8, 8, 512) 2359808 \n \n block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 \n \n flatten (Flatten) (None, 8192) 0 \n \n dense (Dense) (None, 1000) 8193000 \n \n dense_1 (Dense) (None, 1000) 1001000 \n \n dense_2 (Dense) (None, 5) 5005 \n \n=================================================================\nTotal params: 23,913,693\nTrainable params: 9,199,005\nNon-trainable params: 14,714,688\n_________________________________________________________________\nDownloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n11490434/11490434 [==============================] - 1s 0us/step\nEpoch 1/15\n938/938 [==============================] - 28s 29ms/step - loss: 0.1610 - accuracy: 0.9517 - val_loss: 0.0541 - val_accuracy: 0.9823\nEpoch 2/15\n938/938 [==============================] - 25s 26ms/step - loss: 0.0498 - accuracy: 0.9837 - val_loss: 0.0391 - val_accuracy: 0.9871\nEpoch 3/15\n938/938 [==============================] - 25s 27ms/step - loss: 0.0331 - accuracy: 0.9896 - val_loss: 0.0328 - val_accuracy: 0.9886\nEpoch 4/15\n938/938 [==============================] - 25s 27ms/step - loss: 0.0251 - accuracy: 0.9919 - val_loss: 0.0342 - val_accuracy: 0.9887\nEpoch 5/15\n938/938 [==============================] - 25s 27ms/step - loss: 0.0189 - accuracy: 0.9940 - val_loss: 0.0281 - val_accuracy: 0.9913\nEpoch 6/15\n938/938 [==============================] - 25s 26ms/step - loss: 0.0154 - accuracy: 0.9951 - val_loss: 0.0289 - val_accuracy: 0.9911\nEpoch 7/15\n938/938 [==============================] - 25s 26ms/step - loss: 0.0117 - accuracy: 0.9959 - val_loss: 0.0289 - val_accuracy: 0.9913\nEpoch 8/15\n938/938 [==============================] - 25s 27ms/step - loss: 0.0086 - accuracy: 0.9973 - val_loss: 0.0317 - val_accuracy: 0.9906\nEpoch 9/15\n938/938 [==============================] - 25s 27ms/step - loss: 0.0082 - accuracy: 0.9973 - val_loss: 0.0340 - val_accuracy: 0.9905\nEpoch 10/15\n938/938 [==============================] - 26s 28ms/step - loss: 0.0067 - accuracy: 0.9978 - val_loss: 0.0409 - val_accuracy: 0.9884\nEpoch 11/15\n938/938 [==============================] - 25s 26ms/step - loss: 0.0055 - accuracy: 0.9979 - val_loss: 0.0420 - val_accuracy: 0.9912\nEpoch 12/15\n938/938 [==============================] - 27s 28ms/step - loss: 0.0050 - accuracy: 0.9980 - val_loss: 0.0395 - val_accuracy: 0.9899\nEpoch 13/15\n938/938 [==============================] - 25s 27ms/step - loss: 0.0048 - accuracy: 0.9985 - val_loss: 0.0473 - val_accuracy: 0.9885\nEpoch 14/15\n938/938 [==============================] - 26s 27ms/step - loss: 0.0045 - accuracy: 0.9985 - val_loss: 0.0459 - val_accuracy: 0.9893\nEpoch 15/15\n938/938 [==============================] - 25s 26ms/step - loss: 0.0029 - accuracy: 0.9990 - val_loss: 0.0438 - val_accuracy: 0.9908\n313/313 [==============================] - 2s 6ms/step - loss: 0.0438 - accuracy: 0.9908\nTest accuracy: 0.9908\nAccuracy on Train Data: 99.91%\nAccuracy on Test Data: 99.08%\n313/313 [==============================] - 2s 5ms/step\nConfusion Matrix:\n [[ 974 0 1 0 0 1 1 2 1 0]\n [ 0 1130 0 0 0 0 2 2 0 1]\n [ 1 0 1016 2 0 0 0 12 1 0]\n [ 0 0 1 1005 0 4 0 0 0 0]\n [ 0 0 0 0 968 0 4 0 1 9]\n [ 0 0 0 7 0 884 1 0 0 0]\n [ 1 1 0 0 1 2 950 0 3 0]\n [ 0 2 3 1 0 0 0 1020 1 1]\n [ 2 0 1 1 0 3 0 1 962 4]\n [ 0 0 0 0 2 3 1 4 0 999]]\n\nClassification Report:\n precision recall f1-score support\n\n 0 1.00 0.99 0.99 980\n 1 1.00 1.00 1.00 1135\n 2 0.99 0.98 0.99 1032\n 3 0.99 1.00 0.99 1010\n 4 1.00 0.99 0.99 982\n 5 0.99 0.99 0.99 892\n 6 0.99 0.99 0.99 958\n 7 0.98 0.99 0.99 1028\n 8 0.99 0.99 0.99 974\n 9 0.99 0.99 0.99 1009\n\n accuracy 0.99 10000\n macro avg 0.99 0.99 0.99 10000\nweighted avg 0.99 0.99 0.99 10000\n\n313/313 [==============================] - 2s 5ms/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"name":"stdout","text":"313/313 [==============================] - 2s 5ms/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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5o3b57eeustzZ07V8WLF7ddKyf9sxLg6dOnNWbMGLm4uGjw4MEqVKiQAgMDn3v8fPnyKUWKFK/19W/2e4htjRs31pw5c2zXBG7evFmbNm2SpHhbzv9514cahhEvxwcQvyhZABIER0dHjRkzRlevXtW0adNs4xUqVFCaNGm0bNmy5/5wvWjRIklS7dq1bc9Jmzatli9f/so/kNepU0eStGTJkmjtnzZt2mfewPS/v81+mbp168rJyUkrV67U4cOHdfr0aTVt2tRun7x58yo0NFR+fn7P/HiVxQ5y5colq9X61Glf169f1927d5UrV64Yv+bzNG/eXIGBgbp///5T7+3fnvym/7+f1+h8TqNbFqV/7tP175L6ZKGFvHnz6tSpU4qIiIj2a0nS6tWr1bp1a02cOFENGzZUtWrVVKFChWd+faRLl05t27bV8uXL9ddff+ntt9/WsGHD7PbJmzevevXqpc2bN+vo0aMKDw/XxIkTn3t8Nzc3ValSRTt37tRff/0Vo+wJ5T1E15PFR160HP2dO3e0detW9e/fX8OHD1e9evVUrVo15cmT56l9Y/J1kytXLp06deqp8ZMnT9q2A0i+KFkAEozKlSurVKlSmjx5su3GtG5uburdu7dOnTr1zPtSbdiwQQsWLJC/v7/tGgs3Nzf169dPJ06cUL9+/Z75m+IlS5Zo//79z81StmxZ1ahRQ3PnztX333//1Pbw8HC7m6TmzZtXJ0+e1M2bN21jR44c0a+//hrt9y/9c42Lv7+/Vq1apRUrVsjJyUl169a126dx48bau3evfvrpp6eef/fuXUVGRsbomNI/p/FJemplwq+++kqS9N5778X4NZ/n3Xff1ciRIzVt2jR5eXk9d78nP0D/+xqzBw8eaOHChS89hru7u6SnC9qzlC9f3q6kPvnhu0GDBgoODrYr/U+8aPbB0dHxqe1Tp059qvDfunXL7rGHh4fy5cunsLAwSf+sEvjfGzTnzZtXqVKlsu3zPEOHDpVhGGrZsqXdzPATBw8efOHnMSG8h+goXry4vL29NXny5Kf+rp/kfzKD9N/386xVOGPydVOrVi3t379fe/futY09ePBA33zzjXLnzm2bWQeQPLGEO4AEpU+fPmrUqJEWLFigTp06SZL69++vwMBAjR07Vnv37lWDBg3k6uqq3bt3a8mSJSpUqNBTPzD26dNHx44d08SJE7Vt2zY1bNhQXl5eCgoK0vfff6/9+/drz549L8yyaNEiVa9eXfXr11edOnVUtWpVubu768yZM1qxYoWuXbtmO7Xso48+0ldffSV/f3+1a9dON27c0KxZs/Tmm2/aFiKIriZNmujDDz/UjBkz5O/v/9QF/X369NG6detUu3ZttWnTRr6+vnrw4IH+/PNPrV69WhcvXrQ7vTA6ihYtqtatW+ubb77R3bt3ValSJe3fv18LFy5U3bp19e6778bo9V7EwcFBgwYNeul+1atXV86cOdWuXTv16dNHjo6OmjdvnjJmzKjLly+/8Ll58+ZVmjRpNGvWLKVKlUru7u4qXbp0jK4la9WqlRYtWqSePXtq//79euedd/TgwQP9/PPP+uSTT/TBBx8883m1a9fW4sWL5enpqcKFC2vv3r36+eeflT59erv9ChcurMqVK8vX11fp0qXTgQMHtHr1an366aeSpNOnT6tq1apq3LixChcurBQpUmjt2rW6fv36C2cApX+Wq58+fbo++eQTFSxYUC1btlT+/Pl1//59bd++XevWrdOoUaOe+/yE8B6iw8HBQTNnzlSdOnVUrFgxtW3bVlmyZNHJkyd17Ngx/fTTT0qdOrXtWrCIiAhly5ZNmzdv1oULF556PV9fX0n/3LC8adOmSpkyperUqWMrX//Wv39/LV++XDVr1lS3bt2ULl06LVy4UBcuXNCaNWvk4MDvsYFkzbyFDQEkV8+7GbFh/LOsd968eY28efPa3Ug4KirKmD9/vlG+fHkjderUhouLi/Hmm28aw4cPN0JDQ597rNWrVxvVq1c30qVLZ6RIkcLIkiWL0aRJE2P79u3Ryvrw4UNjwoQJRsmSJQ0PDw/DycnJyJ8/v9G1a1e7G9IahmEsWbLEdiPfYsWKGT/99NMLb0b8PCEhIYarq6shyViyZMkz97l//74xYMAAI1++fIaTk5ORIUMGo1y5csaECROM8PDwF76nZy3hbhj/3Ix4+PDhhre3t5EyZUojR44cL7wZcXQ973j/9rzPy8GDB43SpUsbTk5ORs6cOY2vvvoqWku4G4Zh/PDDD0bhwoWNFClSvPLNiB8+fGgMHDjQ9jnx8vIyGjZsaJw7d862j/6z/PmdO3eMtm3bGhkyZDA8PDwMf39/4+TJk08tAT5q1CijVKlSRpo0aQxXV1ejYMGCxhdffGH7+wsODja6dOliFCxY0HB3dzc8PT2N0qVLG6tWrYpW9iefv+bNmxtZs2Y1UqZMaaRNm9aoWrWqsXDhQrsbV5v9Hl51Cfcndu/ebVSrVs1IlSqV4e7ubrz99tt2N+/++++/jXr16hlp0qQxPD09jUaNGhlXr1596n0bxj+3SMiWLZvh4OAQ7ZsRp0mTxnBxcTFKlSr13JsRBwQE2I0/+Zp/1m0GACR+FsPgiksAAAAAiC3MZQMAAABALKJkAQAAAEAsomQBAAAAQCyiZAEAAABALKJkAQAAAEAsomQBAAAAQCxKdjcjtlqtunr1qlKlSiWLxWJ2HAAAAAAmMQxD9+/fV9asWWP1JuLJrmRdvXpVOXLkMDsGAAAAgATir7/+Uvbs2WPt9ZJdyUqVKpWkfz6RqVOnNjkNAAAAALOEhIQoR44cto4QW5JdyXpyimDq1KkpWQAAAABi/TIiFr4AAAAAgFhEyQIAAACAWETJAgAAAIBYRMkCAAAAgFhEyQIAAACAWETJAgAAAIBYRMkCAAAAgFhEyQIAAACAWETJAgAAAIBYRMkCAAAAgFhEyQIAAACAWETJAgAAAIBYRMkCAAAAgFhEyQIAAACAWETJAgAAAIBYRMkCAAAAgFhEyQIAAACAWETJAgAAAIBYRMkCAAAAgFhEyQIAAACAWETJAgAAAIBYRMkCAAAAgFhkasnauXOn6tSpo6xZs8pisej7779/6XO2b9+u4sWLy9nZWfny5dOCBQviPCcAAAAARJepJevBgwcqWrSopk+fHq39L1y4oPfee0/vvvuuDh8+rB49eqh9+/b66aef4jgpAAAAAERPCjMPXrNmTdWsWTPa+8+aNUve3t6aOHGiJKlQoULavXu3Jk2aJH9//7iKCQAAACAJMgwjTl7X1JIVU3v37pWfn5/dmL+/v3r06PHc54SFhSksLMz2OCQk5J8/zK4sOTySJJ2976NfbtfW4whnOYdJltgODgAAACDB2XFic5y8bqIqWUFBQcqcObPdWObMmRUSEqJHjx7J1dX1qeeMGTNGw4cPf2r83NX0OvqwlR5HuCncKa0kyVFSpFOcRAcAAACQwBQpUF3a902sv26iKlmvYsCAAerZs6ftcUhIiHLkyKGt97rJ1cld+k+pcg67I0myMp0FAAAAJCkRkeFKmeL/CoBr1MM4OU6iKlleXl66fv263dj169eVOnXqZ85iSZKzs7OcnZ1f+LrOYXcka5i8rm6Qy/1z+r5oQ50oUEK9qr+hWkWyxFp+AAAAAOb47rvv9OmnvbRlyxa9+eabkv6ZgOmxNPaPlahKVtmyZbVx40a7sS1btqhs2bKv/JpZLs3VwXLXVKnlAFXP/c9n2O8lzwEAAACQOISHh6tv376aMmWKJKlRo0bav3+/PDw84uyYppas0NBQnT171vb4woULOnz4sNKlS6ecOXNqwIABunLlihYtWiRJ6tSpk6ZNm6a+ffvqo48+0i+//KJVq1Zpw4YNr3T8lOF35DGzjb7IXT1W3g8AAACAhOPSpUtq3Lix9u/fbxt7++2342xVwSdMvU/WgQMH5OPjIx8fH0lSz5495ePjoyFDhkiSrl27psuXL9v29/b21oYNG7RlyxYVLVpUEydO1Ny5c19r+fbqFCwAAAAgyVm/fr18fHxsBcvJyUkzZszQ8uXLlSpVqjg9tqkzWZUrV35hi1ywYMEznxMYGBiHqQAAAAAkVpGRkRo0aJDGjh1rG/P29lZAQIB8fX3jJUOiuiYLAAAAAJ7nypUratasmXbt2mUbq1u3rubPn680adLEWw5TTxcEAAAAgNhy/vx57dmzR5KUIkUKTZo0Sd999128FiyJkgUAAAAgiXjnnXc0atQo5ciRQ7t27VKPHj1kscT/DXApWQAAAAASpeDgYFmtVruxvn376o8//lCZMmVMSkXJAgAAAJAIbd++XUWKFNG4cePsxh0cHOL99MD/omQBAAAASDSsVqtGjx6tqlWrKigoSAMHDtTOnTvNjmUnWa8u6GANMzsCAAAAgGgKDg5Wy5YttWnTJttYlSpVVLBgQRNTPS1Zz2RlubrB7AgAAAAAomHPnj3y8fGxFSyLxaJhw4Zp06ZNypQpk8np7CXbmSznsLtKe/ew2TEAAAAAvIBhGPrqq6/Uv39/RUZGSpIyZsyoZcuWyc/Pz+R0z5ZsSxYAAACAhO3evXtq3bq1fvjhB9tYxYoVtXz5cmXNmtXEZC+WrE8XDHNK1m8fAAAASNBSpEihM2fO2B4PGDBAW7duTdAFS0rmJWt91VRmRwAAAADwHO7u7goICFDOnDm1YcMGjR49WilSJPyT8RJ+wjhitUhnfDKYHQMAAADA/xcSEqKQkBBlz57dNla4cGGdOXNGTk5OJiaLmWQ9k/VpsU/NjgAAAABA0pEjR+Tr66t69eopLMz+VkuJqWBJybxkVc9d3ewIAAAAQLJmGIbmzJmj0qVL6+zZszpw4IAGDhxodqzXkmxPFwQAAABgrtDQUHXu3FlLliyxjRUvXlyffPKJialeX7KeyQIAAABgjuPHj6tUqVJ2BeuTTz7Rr7/+qjx58piY7PVRsgAAAADEq8WLF6tkyZI6ceKEJMnDw0MrVqzQ9OnT5eLiYnK618fpggAAAADihWEY6tixo+bMmWMbK1KkiFavXq0CBQqYmCx2MZMFAAAAIF5YLBalT5/e9rhdu3b67bffklTBkpjJAgAAABCPRo4cqcOHD6tZs2Zq1aqV2XHiBCULAAAAQJwICwvTb7/9pooVK9rGUqRIoY0bN8pisZiYLG5xuiAAAACAWHfhwgWVL19e1apV06FDh+y2JeWCJVGyAAAAAMSyH374QT4+Pjp48KDCw8PVsmVLWa1Ws2PFG0oWAAAAgFgRERGhXr16qW7durp3754kKV++fFq6dKkcHJJP9eCaLAAAAACv7a+//lKTJk20d+9e21jDhg01d+5ceXp6mpgs/iWfOgkAAAAgTvz444/y8fGxFayUKVNq6tSpWrVqVbIrWBIzWQAAAABew+TJk/XZZ5/ZHufKlUsBAQEqWbKkianMxUwWAAAAgFdWvnx5pUyZUpJUp04dBQYGJuuCJTGTBQAAAOA1lCxZUpMnT9ajR4/Us2fPJL88e3RQsgAAAABES1RUlBYuXKhWrVopRYr/qxKffPKJiakSHk4XBAAAAPBS169fV40aNdSuXTsNGzbM7DgJGiULAAAAwAvt3LlTPj4++vnnnyVJY8eO1cWLF80NlYBRsgAAAAA8k9Vq1Zdffql3331X165dkyR5eXlpy5Ytyp07t7nhEjCuyQIAAADwlFu3bqlVq1bauHGjbaxKlSpatmyZMmfObGKyhI+ZLAAAAAB29u3bJx8fH1vBslgsGjJkiDZv3kzBigZmsgAAAADY/PLLL/L391dkZKQkKWPGjFq6dKmqVatmcrLEg5ksAAAAADblypVTkSJFJEkVKlRQYGAgBSuGKFkAAAAAbFxcXBQQEKBBgwZp27ZtypYtm9mREh1KFgAAAJBMGYahWbNm6eTJk3bjefPm1ciRI+1uOIzoo2QBAAAAydD9+/fVrFkzde7cWQ0bNtTDhw/NjpRkULIAAACAZOaPP/5QiRIltHLlSknSsWPH9MMPP5icKumgZAEAAADJhGEYmjdvnkqXLq3Tp09LklKnTq3Vq1erWbNmJqdLOjjJEgAAAEgGHjx4oC5dumjhwoW2MR8fHwUEBChv3rwmJkt6mMkCAAAAkrgTJ06odOnSdgWrc+fO2rNnDwUrDjCTBQAAACRht27dUpkyZRQSEiJJcnd315w5czg9MA4xkwUAAAAkYenTp1efPn0kSW+99ZYOHDhAwYpjzGQBAAAASdznn38ud3d3dezYUW5ubmbHSfKYyQIAAACSkNWrV2vatGl2Yw4ODvrss88oWPGEmSwAAAAgCQgLC1OfPn00depUOTo6ysfHR+XLlzc7VrLETBYAAACQyF28eFHvvPOOpk6dKkmKiorSmjVrTE6VfFGyAAAAgERs3bp18vHx0e+//y5JcnZ21qxZszRx4kSTkyVflCwAAAAgEYqIiFDfvn31wQcf6O7du5KkvHnzau/everYsaMsFou5AZMxrskCAAAAEpm///5bTZo00Z49e2xjDRo00LfffitPT08Tk0FiJgsAAABIdJo1a2YrWClTptSUKVMUEBBAwUogKFkAAABAIjNjxgy5uLgoV65c2r17t7p168bpgQkIpwsCAAAAiUyRIkW0bt06+fr6Kl26dGbHwX8wkwUAAAAkYNu2bVPdunUVHh5uN16tWjUKVgJFyQIAAAASIKvVqlGjRsnPz08//PCD+vXrZ3YkRBOnCwIAAAAJzM2bN9WyZUv99NNPtrHjx48rIiJCKVOmNDEZooOZLAAAACAB2b17t3x8fGwFy8HBQSNGjNDGjRspWIkEM1kAAABAAmC1WjVx4kQNGDBAUVFRkqTMmTNr2bJlqlKlisnpEBOULAAAAMBkt2/fVps2bfS///3PNlapUiUtX75cWbJkMTEZXgWnCwIAAAAmmzFjhl3BGjhwoH7++WcKViLFTBYAAABgsn79+mnjxo06ffq0lixZoho1apgdCa+BkgUAAADEM6vVKgeH/zupLGXKlFq1apUMw1COHDlMTIbYwOmCAAAAQDw6dOiQ3n77bR0+fNhuPHv27BSsJIKSBQAAAMQDwzA0a9YslStXTseOHVOjRo107949s2MhDlCyAAAAgDgWGhqqDz/8UJ07d1ZYWJgkKW3atAoNDTU5GeICJQsAAACIQ0ePHlXJkiW1bNky21jXrl21a9cuZcuWzcRkiCuULAAAACCOLFy4UKVKldLJkyclSalSpVJAQIC+/vprOTs7m5wOcYXVBQEAAIBY9vDhQ3Xt2lXz5s2zjRUrVkwBAQHKly+fickQH5jJAgAAAGLZ8ePHtWjRItvjjz/+WHv27KFgJROULAAAACCWlShRQuPHj5e7u7uWLFmi2bNny9XV1exYiCecLggAAAC8psePHytlypRydHS0jXXv3l3169dXzpw5TUwGMzCTBQAAALyGc+fOqVy5cho5cqTduMVioWAlU8xkAQAAAK/ou+++U9u2bRUSEqLDhw+rfPnyqlatmtmxYDJmsgAAAIAYCg8PV48ePdSgQQOFhIRIkvLnz6/MmTObnAwJATNZAAAAQAxcunRJjRs31v79+21jTZs21TfffKNUqVKZmAwJBTNZAAAAQDStX79ePj4+toLl5OSkGTNmaNmyZRQs2FCyAAAAgJeIiIhQv379VKdOHd25c0eS5O3trT179qhz586yWCwmJ0RCQskCAAAAXiIyMlI//fST7XG9evV06NAh+fr6mpgKCRUlCwAAAHgJV1dXrVq1SunSpdOkSZO0Zs0apUmTxuxYSKBY+AIAAAD4j6ioKAUHB9utFligQAFduHBBqVOnNjEZEgNmsgAAAIB/CQoKUrVq1VS9enU9evTIbhsFC9FByQIAAAD+v+3bt8vHx0fbtm3TH3/8oe7du5sdCYkQJQsAAADJntVq1ejRo1W1alUFBQVJkrJkyaIPP/zQ5GRIjLgmCwAAAMlacHCwWrZsqU2bNtnG/Pz8tHTpUmXKlMnEZEismMkCAABAsrVnzx75+PjYCpbFYtGwYcO0adMmChZeGTNZAAAASJYmTZqkvn37KjIyUpKUKVMmLVu2TFWrVjU5GRI7ShYAAACSpXv37tkKVsWKFbV8+XJlzZrV5FRICihZAAAASJYGDx6sPXv2qESJEhoxYoRSpOBHY8QOvpIAAACQ5BmGoT/++ENFixa1jTk6Omrjxo2UK8Q6Fr4AAABAkhYSEqImTZqoRIkS2rdvn902ChbiAiULAAAASdaRI0fk6+urgIAARUZGqkmTJnr06JHZsZDEUbIAAACQ5BiGoTlz5qh06dI6e/asJMnT01OTJ0+Wq6uryemQ1DE/CgAAgCQlNDRUnTt31pIlS2xjvr6+WrVqlfLkyWNiMiQXzGQBAAAgyTh+/LhKlSplV7C6dOmiX3/9lYKFeMNMFgAAAJKEtWvX6sMPP9TDhw8lSR4eHpo7d66aNGlicjIkN5QsAAAAJAne3t6KioqSJBUpUkSrV69WgQIFTE6F5IiSBQAAgCShWLFimjp1qn777TdNnTqVBS5gGq7JAgAAQKK0adMmRURE2I116NBBc+fOpWDBVJQsAAAAJCphYWHq0qWLatasqQEDBpgdB3gKJQsAAACJxvnz51W+fHnNmDFDkjRx4kQdOHDA5FSAPUoWAAAAEoXvv/9exYsX18GDByVJLi4umjNnjnx9fU1OBthj4QsAAAAkaBEREerfv7+++uor21j+/PkVEBCgokWLmpgMeDbTZ7KmT5+u3Llzy8XFRaVLl9b+/ftfuP/kyZP1xhtvyNXVVTly5NBnn32mx48fx1NaAAAAxKfLly+rYsWKdgWrUaNGOnDgAAULCZapJWvlypXq2bOnhg4dqkOHDqlo0aLy9/fXjRs3nrn/smXL1L9/fw0dOlQnTpzQt99+q5UrV+rzzz+P5+QAAACIa0ePHpWPj4/27dsnSXJyctK0adO0cuVKpU6d2uR0wPOZWrK++uordejQQW3btlXhwoU1a9Ysubm5ad68ec/cf8+ePSpfvryaN2+u3Llzq3r16mrWrNlLZ78AAACQ+BQoUED58uWTJOXOnVu//vqrunTpIovFYnIy4MVMK1nh4eE6ePCg/Pz8/i+Mg4P8/Py0d+/eZz6nXLlyOnjwoK1UnT9/Xhs3blStWrWee5ywsDCFhITYfQAAACDhc3Jy0qpVq9SqVSsdOnRIJUqUMDsSEC2mLXwRHBysqKgoZc6c2W48c+bMOnny5DOf07x5cwUHB6tChQoyDEORkZHq1KnTC08XHDNmjIYPHx6r2QEAABD7fv75Z2XOnFlFihSxjeXKlUsLFy40MRUQc6YvfBET27dv1+jRozVjxgwdOnRI3333nTZs2KCRI0c+9zkDBgzQvXv3bB9//fVXPCYGAADAy0RFRWnYsGGqXr26GjVqpPv375sdCXgtps1kZciQQY6Ojrp+/brd+PXr1+Xl5fXM5wwePFgtW7ZU+/btJUlFihTRgwcP9PHHH2vgwIFycHi6Mzo7O8vZ2Tn23wAAAABe2/Xr19WiRQtt3bpVknTq1CnNmjVLffr0MTkZ8OpMm8lycnKSr6+v7R+UJFmtVm3dulVly5Z95nMePnz4VJFydHSUJBmGEXdhAQAAEOt27twpHx8f28+DDg4OGj16tHr16mVyMuD1mHoz4p49e6p169YqUaKESpUqpcmTJ+vBgwdq27atJKlVq1bKli2bxowZI0mqU6eOvvrqK/n4+Kh06dI6e/asBg8erDp16tjKFgAAABI2q9WqcePGaeDAgbJarZIkLy8vLV++XJUrVzY3HBALTC1ZTZo00c2bNzVkyBAFBQWpWLFi2rRpk20xjMuXL9vNXA0aNEgWi0WDBg3SlStXlDFjRtWpU0dffPGFWW8BAAAAMXDr1i21atVKGzdutI1VrVpVS5cufWpBNCCxshjJ7Dy7kJAQeXp6anKLReq+pKXZcQAAAJKNx48f680339T58+clSRaLRUOGDNHgwYM5KwmmeNIN7t27F6s3uE5UqwsCAAAg8XJxcVGnTp0kSRkzZtRPP/2kYcOGUbCQ5Jh6uiAAAACSl169eun+/fvq2LGjsmXLZnYcIE4wkwUAAIA4cfDgQc2aNctuzMHBQSNGjKBgIUljJgsAAACxyjAMzZw5U5999pkiIyP1xhtv6N133zU7FhBvmMkCAABArLl//76aNWumLl26KDw8XFarVZMnTzY7FhCvKFkAAACIFX/88YdKlCihlStX2sZ69OihgIAAE1MB8Y/TBQEAAPBaDMPQ/Pnz1aVLFz1+/FiSlDp1as2fP1/169c3OR0Q/yhZAAAAeGUPHjxQly5dtHDhQtuYj4+PAgIClDdvXhOTAebhdEEAAAC8so8++siuYHXu3Fl79uyhYCFZo2QBAADglQ0fPlzu7u7y8PDQsmXLNGPGDLm4uJgdCzAVpwsCAADglRUsWFArVqxQ/vz59cYbb5gdB0gQmMkCAABAtJw5c0atW7e2LW7xRO3atSlYwL8wkwUAAICXCggIULt27XT//n25ublp5syZZkcCEixmsgAAAPBcYWFh6tatmxo3bqz79+9LkrZv366QkBCTkwEJFyULAAAAz3Tx4kW98847mjp1qm2sefPm+v3335U6dWoTkwEJGyULAAAAT1m3bp18fHz0+++/S5KcnZ01a9YsLVmyRB4eHianAxI2rskCAACATUREhD7//HNNmDDBNpY3b14FBATIx8fHxGRA4sFMFgAAAGy+/fZbu4LVoEEDHTx4kIIFxAAlCwAAADbt27dXpUqVlDJlSn399dcKCAiQp6en2bGARIXTBQEAAGCTIkUKLV++XH/99ZdKlSpldhwgUWImCwAAIJm6du2a/P39tX//frvxLFmyULCA18BMFgAAQDL0yy+/qFmzZrpx44ZOnTqlwMBApU2b1uxYQJLATBYAAEAyYrVaNXLkSPn5+enGjRuSpMjISP31118mJwOSDmayAAAAkombN2/qww8/1ObNm21j1atX15IlS5QxY0YTkwFJCzNZAAAAycDu3bvl4+NjK1gODg4aOXKkfvzxRwoWEMuYyQIAAEjCrFarJk6cqAEDBigqKkqSlDlzZi1btkxVqlQxOR2QNFGyAAAAkrAzZ85o0KBBtoJVuXJlLV++XF5eXiYnA5IuThcEAABIwt544w1NmjRJkjRo0CBt2bKFggXEMWayAAAAkhDDMGS1WuXo6Ggb69y5s8qWLSsfHx8TkwHJBzNZAAAAScS9e/fUsGFDDRo0yG7cYrFQsIB4xEwWAABAEnDo0CE1atRI58+flyRVqFBB7733nsmpgOSJmSwAAIBEzDAMzZo1S+XKlbMVrLRp08pisZicDEi+mMkCAABIpO7fv6+OHTtq+fLltrGSJUtq1apVyp07t3nBgGSOmSwAAIBE6M8//1TJkiXtCla3bt20e/duChZgMkoWAABAIrNgwQKVLl1ap06dkiSlSpVKAQEBmjJlipycnExOB4DTBQEAABKRyMhITZ8+XY8ePZIkFStWTAEBAcqXL5/JyQA8wUwWAABAIpIiRQqtWrVKadKkUceOHbV3714KFpDAMJMFAACQwN2/f1+pUqWyPfb29taxY8eUNWtWE1MBeB5msgAAABKox48fq1OnTipTpowePHhgt42CBSRclCwAAIAE6OzZsypbtqxmz56t48ePq1OnTjIMw+xYAKKBkgUAAJDArFmzRr6+vjp8+LAkycXFRVWqVOEGw0AiwTVZAAAACUR4eLj69u2rKVOm2MYKFCiggIAAvf322yYmAxATlCwAAIAE4NKlS2rcuLH2799vG2vatKm++eYbu0UvACR8nC4IAABgsvXr18vHx8dWsJycnDRjxgwtW7aMggUkQsxkAQAAmOzPP//UnTt3JEl58uRRQECAihcvbnIqAK+KkgUAAGCyfv36adeuXXJxcdG8efOUJk0asyMBeA2ULAAAgHh2+fJl5cyZ0/bYwcFBq1evlqurKysIAkkA12QBAADEk6ioKA0ZMkT58uXTzp077ba5ublRsIAkgpIFAAAQD4KCglStWjWNHDlSERERatq0qW7dumV2LABxgNMFAQAA4tj27dvVrFkzBQUFSZIcHR3VvXt3pU2b1uRkAOICJQsAACCOWK1Wffnllxo8eLCsVqskKUuWLFqxYoUqVqxocjoAcYWSBQAAEAeCg4PVsmVLbdq0yTbm5+enpUuXKlOmTCYmAxDXuCYLAAAglu3fv18+Pj62gmWxWDR8+HBt2rSJggUkA8xkAQAAxDJ3d3fbohaZMmXSsmXLVLVqVZNTAYgvzGQBAADEsjfffFMzZ85UxYoVFRgYSMECkhlKFgAAwGs6fPiwwsLC7MZat26tbdu2KWvWrCalAmAWShYAAMArMgxDU6dOValSpdS7d++ntjs48KMWkBzxLx8AAOAV3Lt3T40bN1a3bt0UERGhadOm2a0kCCD5YuELAACAGDp8+LAaNWqks2fP2sZ69erFtVcAJFGyAAAAos0wDM2dO1ddu3a1XYOVJk0aLViwQB988IHJ6QAkFJQsAACAaAgNDVXnzp21ZMkS21iJEiW0atUqeXt7m5gMQEJDyQIAAHiJK1euqFq1ajpx4oRt7NNPP9WECRPk7OxsYjIACRElCwAA4CUyZcqk9OnTS5JSpUqluXPnqnHjxianApBQsbogAADAS6RMmVIrVqyQn5+fDhw4QMEC8ELMZAEAAPzHqVOn9PjxYxUtWtQ2li1bNm3ZssXEVAASC2ayAAAA/mXFihUqUaKE6tevr7t375odB0AiRMkCAACQ9PjxY33yySdq1qyZQkNDdf78eQ0fPtzsWAASIU4XBAAAyd758+fVqFEjHTp0yDbWsmVLjRo1ysRUABIrZrIAAECytnbtWhUvXtxWsFxcXDRnzhwtXLhQ7u7uJqcDkBgxkwUAAJKl8PBw9e/fX5MmTbKN5c+fXwEBAXYLXgBATFGyAABAsmO1WlWtWjXt3LnTNta4cWPNmTNHqVOnNjEZgKSA0wUBAECy4+DgoIYNG0qSnJycNH36dK1YsYKCBSBWMJMFAACSpU8//VTnz59XixYtVKJECbPjAEhCmMkCAABJ3tWrV7VgwQK7MYvFokmTJlGwAMQ6ZrIAAECStmXLFrVo0UI3b96Ul5eXatSoYXYkAEkcM1kAACBJioqK0rBhw+Tv76+bN29Kkj7//HMZhmFyMgBJHTNZAAAgybl+/bpatGihrVu32sZq1qypRYsWyWKxmJgMQHLATBYAAEhSdu7cKR8fH1vBcnBw0OjRo7V+/XplyJDB5HQAkgNmsgAAQJJgtVo1btw4DRw4UFarVZLk5eWlFStWqFKlSianA5CcULIAAECS0LdvX02cONH2uGrVqlq6dKkyZ85sYioAyRGnCwIAgCShU6dOSp06tSwWi4YOHaqffvqJggXAFMxkAQCAJCFfvnxavHixXF1dVa1aNbPjAEjGmMkCAACJzt27d9WrVy89fPjQbvz999+nYAEwHTNZAAAgUTl48KAaNWqkCxcu6M6dO5o3b57ZkQDADjNZAAAgUTAMQzNmzFC5cuV04cIFSdL333+vv//+2+RkAGCPkgUAABK8+/fvq1mzZurSpYvCw8MlSaVLl1ZgYKCyZ89ucjoAsEfJAgAACdoff/yhEiVKaOXKlbaxHj16aOfOncqVK5eJyQDg2bgmCwAAJEiGYWjevHn69NNP9fjxY0lS6tSpNX/+fNWvX9/kdADwfJQsAACQIP3www9q37697XHx4sW1atUq5c2b18RUAPBynC4IAAASpPfff1/Vq1eXJHXu3Fm//vorBQtAosBMFgAASJAcHBy0ZMkSbd++XY0aNTI7DgBEGzNZAADAdI8ePbLNVv1bxowZKVgAEh1KFgAAMNWZM2dUtmxZzZo1S02aNNHNmzfNjgQAr4WSBQAATBMQECBfX18dOXJEknT79m0FBgaanAoAXs9rlawny6kCAADERFhYmLp27arGjRvr/v37kqSCBQtq//79tsUuACCxinHJslqtGjlypLJlyyYPDw+dP39ekjR48GB9++23sR4QAAAkLRcuXFCFChU0bdo021jz5s31+++/66233jIxGQDEjhiXrFGjRmnBggUaN26cnJycbONvvfWW5s6dG6vhAABA0rJu3ToVL15cBw4ckCQ5Oztr9uzZWrJkiTw8PExOBwCxI8Yla9GiRfrmm2/UokULOTo62saLFi2qkydPxmo4AACQdFy/fl1NmzbV3bt3JUl58+bV3r179fHHH8tisZgbDgBiUYxL1pUrV5QvX76nxq1WqyIiImIlFAAASHoyZ85sO0WwQYMGOnjwoHx8fExOBQCxL8Y3Iy5cuLB27dqlXLly2Y2vXr2ab5QAAMCOYRh2s1Rt27ZV9uzZVa1aNWavACRZMS5ZQ4YMUevWrXXlyhVZrVZ99913OnXqlBYtWqT169fHRUYAAJDIREZGaujQoXr8+LEmTpxoG7dYLKweCCDJi3HJ+uCDD/S///1PI0aMkLu7u4YMGaLixYvrf//7n6pVqxYXGQEAQCJy7do1NWvWTDt27JAklS9fXvXr1zc5FQDEnxiXLEl65513tGXLltjOAgAAErlffvlFzZo1040bNyRJjo6OunbtmsmpACB+xXjhizx58ujWrVtPjd+9e1d58uSJlVAAACBxiYqK0ogRI+Tn52crWNmyZdOOHTvUpUsXk9MBQPyK8UzWxYsXFRUV9dR4WFiYrly5EiuhAABA4nHjxg19+OGHdme5+Pv7a/HixcqYMaOJyQDAHNEuWevWrbP9+aeffpKnp6ftcVRUlLZu3arcuXPHajgAAJCw7dq1S02bNtXVq1clSQ4ODhoxYoQGDBggB4cYnzADAElCtEtW3bp1Jf2zKlDr1q3ttqVMmVK5c+e2Wz0IAAAkbYZhaNCgQbaC5eXlpWXLlundd981ORkAmCvav2KyWq2yWq3KmTOnbty4YXtstVoVFhamU6dOqXbt2jEOMH36dOXOnVsuLi4qXbq09u/f/8L97969qy5duihLlixydnZWgQIFtHHjxhgfFwAAvB6LxaIlS5Yoffr0evfddxUYGEjBAgC9wjVZFy5ciLWDr1y5Uj179tSsWbNUunRpTZ48Wf7+/jp16pQyZcr01P7h4eGqVq2aMmXKpNWrVytbtmy6dOmS0qRJE2uZAADA80VERChlypS2xzly5NDu3buVP39+OTo6mpgMABKOV1rC/cGDB9qxY4cuX76s8PBwu23dunWL9ut89dVX6tChg9q2bStJmjVrljZs2KB58+apf//+T+0/b9483b59W3v27LF9g+c6MAAA4p5hGPr66681Z84c7dmzR6lTp7ZtK1iwoInJACDhiXHJCgwMVK1atfTw4UM9ePBA6dKlU3BwsNzc3JQpU6Zol6zw8HAdPHhQAwYMsI05ODjIz89Pe/fufeZz1q1bp7Jly6pLly764YcflDFjRjVv3lz9+vV77m/PwsLCFBYWZnscEhISg3cLAADu3r2rdu3a6bvvvpMktW/fXitXrpTFYjE5GQAkTDFe9uezzz5TnTp1dOfOHbm6umrfvn26dOmSfH19NWHChGi/TnBwsKKiopQ5c2a78cyZMysoKOiZzzl//rxWr16tqKgobdy4UYMHD9bEiRM1atSo5x5nzJgx8vT0tH3kyJEj2hkBAEjuDh06JF9fX1vBkv45i8RqtZqYCgASthiXrMOHD6tXr15ycHCQo6OjwsLClCNHDo0bN06ff/55XGS0sVqtypQpk7755hv5+vqqSZMmGjhwoGbNmvXc5wwYMED37t2zffz1119xmhEAgKTAMAzNmjVLZcuW1fnz5yVJadOm1bp16zRu3DiuvwKAF4jx6YIpU6a03fciU6ZMunz5sgoVKiRPT88YFZgMGTLI0dFR169ftxu/fv26vLy8nvmcLFmyKGXKlHbf2AsVKqSgoCCFh4fLycnpqec4OzvL2dk52rkAAEju7t+/r44dO2r58uW2sZIlS2rVqlVcCw0A0RDjmSwfHx/9/vvvkqRKlSppyJAhWrp0qXr06KG33nor2q/j5OQkX19fbd261TZmtVq1detWlS1b9pnPKV++vM6ePWt3isLp06eVJUuWZxYsAAAQM3/++adKlChhV7C6deum3bt3U7AAIJpiXLJGjx6tLFmySJK++OILpU2bVp07d9bNmzc1e/bsGL1Wz549NWfOHC1cuFAnTpxQ586d9eDBA9tqg61atbJbGKNz5866ffu2unfvrtOnT2vDhg0aPXq0unTpEtO3AQAAnmHr1q06ffq0JCl16tRavXq1pkyZwi8zASAGYny6YIkSJWx/zpQpkzZt2vTKB2/SpIlu3rypIUOGKCgoSMWKFdOmTZtsi2FcvnzZdmqi9M+9OH766Sd99tlnevvtt5UtWzZ1795d/fr1e+UMAADg/3Tv3l07duzQxYsXFRAQoHz58pkdCQASHYthGEZsvNChQ4c0ZMgQrV+/PjZeLs6EhITI09NTk1ssUvclLc2OAwCAqe7cuaO0adPajYWEhMjJyUkuLi4mpQKA+PGkG9y7d8/u/n+vK0anC/7000/q3bu3Pv/8c9tKQydPnlTdunVVsmRJlnMFACARWbZsmXLnzm13fbT0z2mCFCwAeHXRLlnffvutatasqQULFmjs2LEqU6aMlixZorJly8rLy0tHjx7Vxo0b4zIrAACIBY8fP1anTp3UokULhYSEqHnz5rp69arZsQAgyYh2yZoyZYrGjh2r4OBgrVq1SsHBwZoxY4b+/PNPzZo1S4UKFYrLnAAAIBacPXtWZcuWtVusqmbNmvL09DQxFQAkLdEuWefOnVOjRo0kSfXr11eKFCk0fvx4Zc+ePc7CAQCA2LNmzRr5+vrq8OHDkiQXFxfNmzdPCxYskLu7u7nhACAJifbqgo8ePZKbm5skyWKxyNnZ2baUOwAASLjCw8PVt29fTZkyxTb2xhtvKCAgQEWKFDExGQAkTTFawn3u3Lny8PCQJEVGRmrBggXKkCGD3T7dunWLvXQAAOC1XLp0SY0bN9b+/fttY02bNtU333yjVKlSmZgMAJKuaJesnDlzas6cObbHXl5eWrx4sd0+FouFkgUAQAISFham48ePS5KcnJw0ZcoUdezYURaLxeRkAJB0RbtkXbx4MQ5jAACAuFCgQAHNnTtXn3/+uQICAlS8eHGzIwFAkhej+2QBAICE7cqVK3r06JHdWJMmTXTs2DEKFgDEE0oWAABJxObNm1WsWDF17979qW3cXBgA4g8lCwCARC4qKkpDhgxRjRo1FBwcrDlz5mjlypVmxwKAZCtGqwsCAICEJSgoSM2bN9e2bdtsY7Vr11a1atVMTAUAyRszWQAAJFLbt2+Xj4+PrWA5Ojpq7Nix+uGHH5QuXTqT0wFA8vVKJevcuXMaNGiQmjVrphs3bkiSfvzxRx07dixWwwEAgKdZrVaNHj1aVatWVVBQkCQpa9as2r59u/r27SsHB36HCgBmivF34R07dqhIkSL67bff9N133yk0NFSSdOTIEQ0dOjTWAwIAgP8TEhKi9957TwMHDpTVapUkVatWTYGBgapQoYLJ6QAA0iuUrP79+2vUqFHasmWLnJycbONVqlTRvn37YjUcAACw5+7urvDwcEmSxWLRiBEj9OOPPypTpkwmJwMAPBHjkvXnn3+qXr16T41nypRJwcHBsRIKAAA8m6Ojo5YuXaqiRYtqy5YtGjx4sBwdHc2OBQD4lxivLpgmTRpdu3ZN3t7eduOBgYHKli1brAUDAADS7du3dfXqVb311lu2MS8vLwUGBspisZiYDADwPDGeyWratKn69eunoKAgWSwWWa1W/frrr+rdu7datWoVFxkBAEiW9u/fr+LFi+u9997TrVu37LZRsAAg4YpxyRo9erQKFiyoHDlyKDQ0VIULF1bFihVVrlw5DRo0KC4yAgCQrBiGoa+//loVKlTQpUuXdPnyZXXv3t3sWACAaIrx6YJOTk6aM2eOBg8erKNHjyo0NFQ+Pj7Knz9/XOQDACBZuXfvntq1a6c1a9bYxsqWLasxY8aYmAoAEBMxLlm7d+9WhQoVlDNnTuXMmTMuMgEAkCwFBgaqUaNGOnfunG2sV69eGjNmjFKmTGliMgBATMT4dMEqVarI29tbn3/+uY4fPx4XmQAASFYMw9A333yjsmXL2gpWmjRp9P3332vChAkULABIZGJcsq5evapevXppx44deuutt1SsWDGNHz9ef//9d1zkAwAgyfv444/VsWNHhYWFSZJKlCihQ4cO6YMPPjA5GQDgVcS4ZGXIkEGffvqpfv31V507d06NGjXSwoULlTt3blWpUiUuMgIAkKSVLFnS9ueuXbtq9+7dT90qBQCQeMT4mqx/8/b2Vv/+/VW0aFENHjxYO3bsiK1cAAAkGx06dFBgYKCqVKmiRo0amR0HAPCaYjyT9cSvv/6qTz75RFmyZFHz5s311ltvacOGDbGZDQCAJOfhw4davXq13ZjFYtHMmTMpWACQRMS4ZA0YMEDe3t6qUqWKLl++rClTpigoKEiLFy9WjRo14iIjAABJwqlTp1SmTBk1atRI69atMzsOACCOxPh0wZ07d6pPnz5q3LixMmTIEBeZAABIclasWKEOHTooNDRUktSlSxf5+/vL2dnZ5GQAgNgW45L166+/xkUOAACSpMePH6tnz56aOXOmbaxw4cIKCAigYAFAEhWtkrVu3TrVrFlTKVOmfOnpDe+//36sBAMAILE7f/68GjVqpEOHDtnGWrZsqZkzZ8rd3d3EZACAuBStklW3bl0FBQUpU6ZMqlu37nP3s1gsioqKiq1sAAAkWmvXrlXbtm117949SZKLi4umTZumjz76SBaLxeR0AIC4FK2SZbVan/lnAADwtGnTpqlr1662x/nz51dAQICKFi1qYioAQHyJ8eqCixYtst2R/t/Cw8O1aNGiWAkFAEBiVrt2baVNm1aS1LhxYx04cICCBQDJSIxL1r9Pffi3+/fvq23btrESCgCAxCx37txatGiRpk+frhUrVih16tRmRwIAxKMYry5oGMYzzyX/+++/5enpGSuhAABILCIjIzV58mR16tRJHh4etvHatWubmAoAYKZolywfHx9ZLBZZLBZVrVpVKVL831OjoqJ04cIFbkYMAEhWrl69qqZNm2rXrl0KDAzUkiVLWNQCABD9kvVkVcHDhw/L39/f7rd1Tk5Oyp07txo0aBDrAQEASIi2bNmiFi1a6ObNm5KkVatWqV+/fnr77bdNTgYAMFu0S9bQoUMl/XOeeZMmTeTi4hJnoQAASKiioqI0cuRIjRgxQoZhSJKyZ8+uVatWUbAAAJJe4Zqs1q1bx0UOAAASvOvXr6tFixbaunWrbaxmzZpatGiRMmTIYGIyAEBCEq2SlS5dOp0+fVoZMmRQ2rRpX3i++e3bt2MtHAAACcXOnTvVtGlTXbt2TZLk4OCgUaNGqV+/fnJwiPFivQCAJCxaJWvSpElKlSqV7c9c1AsASE5+++03vfvuu7JarZKkLFmyaPny5apUqZLJyQAACVG0Sta/TxFs06ZNXGUBACBBKlmypGrVqqX169eratWqWrp0qTJnzmx2LABAAhXj8xsOHTqkP//80/b4hx9+UN26dfX5558rPDw8VsMBAJAQODg4aOHChRo/frx++uknChYA4IViXLI6duyo06dPS5LOnz+vJk2ayM3NTQEBAerbt2+sBwQAID4ZhqFJkyZp+/btduPp0qVT79695ejoaE4wAECiEeOSdfr0aRUrVkySFBAQoEqVKmnZsmVasGCB1qxZE9v5AACIN3fu3FH9+vXVs2dPNWvWTEFBQWZHAgAkQjEuWYZh2C78/fnnn1WrVi1JUo4cORQcHBy76QAAiCcHDhyQr6+vvv/+e0lSUFCQNm7caG4oAECiFOOSVaJECY0aNUqLFy/Wjh079N5770mSLly4wDnqAIBExzAMTZ8+XeXLl9eFCxckSWnTptX69ev10UcfmZwOAJAYxfhmxJMnT1aLFi30/fffa+DAgcqXL58kafXq1SpXrlysBwQAIK6EhISoQ4cOWrVqlW2sdOnSWrlypXLlymViMgBAYhbjkvX222/brS74xPjx47kYGACQaBw5ckSNGjXSmTNnbGM9evTQ2LFj5eTkZGIyAEBiF+OS9cTBgwd14sQJSVLhwoVVvHjxWAsFAEBcevDggfz8/GzXEnt6emr+/PmqV6+eyckAAElBjEvWjRs31KRJE+3YsUNp0qSRJN29e1fvvvuuVqxYoYwZM8Z2RgAAYpW7u7smTZqkli1bqnjx4lq1apXy5s1rdiwAQBIR44UvunbtqtDQUB07dky3b9/W7du3dfToUYWEhKhbt25xkREAgFj34YcfatmyZfr1118pWACAWBXjmaxNmzbp559/VqFChWxjhQsX1vTp01W9evVYDQcAQGxYvHixjhw5ogkTJtiNN2vWzKREAICkLMYly2q1KmXKlE+Np0yZ0nb/LAAAEoJHjx6pW7dumjt3riTJ19eXYgUAiHMxPl2wSpUq6t69u65evWobu3Llij777DNVrVo1VsMBAPCqTp8+rTJlytgKliTt27fPxEQAgOQixiVr2rRpCgkJUe7cuZU3b17lzZtX3t7eCgkJ0dSpU+MiIwAAMbJq1SqVKFFCf/zxhyTJzc1NCxcu1JQpU0xOBgBIDmJ8umCOHDl06NAhbd261baEe6FCheTn5xfr4QAAiImwsDD17t1b06ZNs40VKlRIAQEBevPNN01MBgBITmJUslauXKl169YpPDxcVatWVdeuXeMqFwAAMXLhwgU1btxYBw4csI21aNFCs2bNkoeHh4nJAADJTbRL1syZM9WlSxflz59frq6u+u6773Tu3DmNHz8+LvMBABAtn332ma1gOTs7a+rUqWrfvr0sFovJyQAAyU20r8maNm2ahg4dqlOnTunw4cNauHChZsyYEZfZAACItpkzZypTpkzKly+f9u3bpw4dOlCwAACmiHbJOn/+vFq3bm173Lx5c0VGRuratWtxEgwAgBcxDMPucZYsWbRp0yYdPHhQxYoVMycUAACKQckKCwuTu7v7/z3RwUFOTk569OhRnAQDAOB5fvzxR5UqVUp37tyxG/fx8VHq1KlNSgUAwD9itPDF4MGD5ebmZnscHh6uL774Qp6enraxr776KvbSAQDwL5GRkRo6dKhGjx4tSWrTpo2+//57TgsEACQo0S5ZFStW1KlTp+zGypUrp/Pnz9se8z85AEBcuXr1qpo3b64dO3bYxgzD0KNHj+x+AQgAgNmiXbK2b98ehzEAAHi+rVu3qnnz5rpx44YkydHRUWPHjlXPnj35BR8AIMGJ9jVZAADEt6ioKI0YMULVqlWzFaxs2bJpx44d6tWrFwULAJAgxeiaLAAA4suNGzf04YcfasuWLbYxf39/LV68WBkzZjQxGQAAL8ZMFgAgQVq3bp2tYDk4OOiLL77Qxo0bKVgAgASPmSwAQILUrl07/fzzz9qxY4eWL1+uypUrmx0JAIBooWQBABKEsLAwOTs72x5bLBbNmTNHDx48kJeXl4nJAACImVc6XXDXrl368MMPVbZsWV25ckWStHjxYu3evTtWwwEAkod9+/bpjTfe0MaNG+3GU6VKRcECACQ6MS5Za9askb+/v1xdXRUYGKiwsDBJ0r1792w3hwQAIDoMw9DkyZP1zjvv6NKlS2rZsqUuX75sdiwAAF5LjEvWqFGjNGvWLM2ZM0cpU6a0jZcvX16HDh2K1XAAgKTr7t27atCggT777DNFRkZKkgoVKiRHR0eTkwEA8HpiXLJOnTqlihUrPjXu6empu3fvxkYmAEASd+jQIfn6+mrt2rW2sb59+2rbtm3Kli2bickAAHh9MS5ZXl5eOnv27FPju3fvVp48eWIlFAAgaTIMQ7NmzVLZsmV1/vx5SVLatGm1bt06jR071u4MCQAAEqsYl6wOHTqoe/fu+u2332SxWHT16lUtXbpUvXv3VufOneMiIwAgCbh//75atGihzp07Kzw8XJJUqlQpBQYGqk6dOianAwAg9sR4Cff+/fvLarWqatWqevjwoSpWrChnZ2f17t1bXbt2jYuMAIAkIDg42G71wO7du2vcuHFycnIyMRUAALEvxjNZFotFAwcO1O3bt3X06FHt27dPN2/e1MiRI+MiHwAgifD29taCBQvk6emp1atXa/LkyRQsAECS9Mo3I3ZyclLhwoVjMwsAIAl5+PChDMOQu7u7baxu3bo6f/680qVLZ2IyAADiVoxL1rvvviuLxfLc7b/88strBQIAJH4nTpxQo0aNVLx4cS1cuNDu/xsULABAUhfjklWsWDG7xxERETp8+LCOHj2q1q1bx1YuAEAitWzZMn388cd68OCBjh07pkqVKqldu3ZmxwIAIN7EuGRNmjTpmePDhg1TaGjoawcCACROjx8/Vo8ePTR79mzb2Jtvvqny5cubmAoAgPgX44UvnufDDz/UvHnzYuvlAACJyNmzZ1W2bFm7gtWmTRvt379fBQsWNDEZAADxL9ZK1t69e+Xi4hJbLwcASCTWrFkjX19fHT58WJLk6uqqefPmaf78+XJzczM3HAAAJojx6YL169e3e2wYhq5du6YDBw5o8ODBsRYMAJCwRUREqE+fPpoyZYpt7I033lBAQICKFCliYjIAAMwV45Ll6elp99jBwUFvvPGGRowYoerVq8daMABAwubo6KhTp07ZHjdt2lTffPONUqVKZWIqAADMF6OSFRUVpbZt26pIkSJKmzZtXGUCACQCDg4OWrx4scqWLatevXqpY8eOL7zFBwAAyUWMSpajo6OqV6+uEydOULIAIJmJiIjQxYsXlT9/fttYhgwZdOzYMTk5OZmYDACAhCXGC1+89dZbOn/+fFxkAQAkUH///bcqV66sypUr68aNG3bbKFgAANiLcckaNWqUevfurfXr1+vatWsKCQmx+wAAJC2bNm1SsWLFtGfPHl29elVt27Y1OxIAAAlatEvWiBEj9ODBA9WqVUtHjhzR+++/r+zZsytt2rRKmzat0qRJwymEAJCEREZGatCgQapVq5Zu3bolScqZM6eGDBlicjIAABK2aF+TNXz4cHXq1Enbtm2LyzwAgATg2rVrat68ubZv324bq127thYuXKh06dKZFwwAgEQg2iXLMAxJUqVKleIsDADAfNu2bVOzZs10/fp1Sf8sejR69Gj17t1bDg6xdg97AACSrBitLsjSvACQtE2YMEH9+vWT1WqVJGXNmlUrV65UhQoVTE4GAEDiEaOSVaBAgZcWrdu3b79WIACAeTJmzGgrWNWrV9eSJUuUMWNGk1MBAJC4xKhkDR8+XJ6ennGVBQBgstatW+vXX39Vjhw59Pnnn8vR0dHsSAAAJDoxKllNmzZVpkyZ4ioLACAeWa1Wbdu2TVWrVrUbnz17NqeHAwDwGqJ9BTP/wwWApOP27duqW7eu/Pz8tHr1arttfL8HAOD1RLtkPVldEACQuO3fv1/FixfX//73P0lS+/btdffuXXNDAQCQhES7ZFmtVk4VBIBEzDAMff3116pQoYIuXbokSUqfPr2WL1+uNGnSmBsOAIAkJEbXZAEAEqd79+6pXbt2WrNmjW2sXLlyWrFihXLkyGFiMgAAkh7uKgkASVxgYKB8fX3tClbv3r21fft2ChYAAHGAmSwASMLWrVunxo0bKywsTJKUJk0aLViwQB988IHJyQAASLooWQCQhPn6+ipVqlQKCwtTiRIltGrVKnl7e5sdCwCAJI2SBQBJWLZs2bR06VKtX79e48ePl7Ozs9mRAABI8hLENVnTp09X7ty55eLiotKlS2v//v3Ret6KFStksVhUt27duA0IAInEypUrde/ePbux6tWr6+uvv6ZgAQAQT0wvWStXrlTPnj01dOhQHTp0SEWLFpW/v79u3LjxwuddvHhRvXv31jvvvBNPSQEg4Xr48KHatWunpk2bql27dtzbEAAAE5lesr766it16NBBbdu2VeHChTVr1iy5ublp3rx5z31OVFSUWrRooeHDhytPnjzxmBYAEp5Tp06pTJkytu+ba9as0datW01OBQBA8mVqyQoPD9fBgwfl5+dnG3NwcJCfn5/27t373OeNGDFCmTJlUrt27V56jLCwMIWEhNh9AEBSsWLFCpUoUUJ//vmnJMnNzU2LFy+2+74KAADil6klKzg4WFFRUcqcObPdeObMmRUUFPTM5+zevVvffvut5syZE61jjBkzRp6enrYP7gkDICl4/PixPvnkEzVr1kyhoaGSpMKFC+v333/Xhx9+aHI6AACSN9NPF4yJ+/fvq2XLlpozZ44yZMgQrecMGDBA9+7ds3389ddfcZwSAOLW+fPnVb58ec2cOdM21rJlS+3fv1+FCxc2MRkAAJBMXsI9Q4YMcnR01PXr1+3Gr1+/Li8vr6f2P3funC5evKg6derYxqxWqyQpRYoUOnXqlPLmzWv3HGdnZ1bUApBknDt3Tr6+vrYVBF1cXDRt2jR99NFHslgsJqcDAACSyTNZTk5O8vX1tbtA22q1auvWrSpbtuxT+xcsWFB//vmnDh8+bPt4//339e677+rw4cOcCgggycuTJ4/teqv8+fPrt99+U7t27ShYAAAkIKbfjLhnz55q3bq1SpQooVKlSmny5Ml68OCB2rZtK0lq1aqVsmXLpjFjxsjFxUVvvfWW3fPTpEkjSU+NA0BSZLFY9O233ypHjhwaPny4UqdObXYkAADwH6aXrCZNmujmzZsaMmSIgoKCVKxYMW3atMm2GMbly5fl4JCoLh0DgFizYcMGOTs7260W6OnpqUmTJpmYCgAAvIjFSGZ3rAwJCZGnp6cmt1ik7ktamh0HAJ4pMjJSgwcP1pdffqkMGTLo8OHDypYtm9mxAABIUp50g3v37sXq2SFMEQFAAnPlyhVVqVJFX375paR/bnfxzTffmJwKAABEl+mnCwIA/s+WLVvUokUL3bx5U9I/K6eOGzdOPXr0MDcYAACINmayACABiIqK0tChQ+Xv728rWNmzZ9fOnTv12WefsXogAACJCDNZAGCy69evq0WLFna3s6hZs6YWLVoU7RuvAwCAhIOSBQAmioqKUuXKlXXy5ElJkoODg7744gv17duXlVUBAEik+D84AJjI0dFRI0eOlCRlyZJFv/zyi/r370/BAgAgEWMmCwBM1rBhQ82cOVP16tWz3SMQAAAkXvyqFADi0Z49e/T5558/Nd6pUycKFgAASQQzWQAQDwzD0FdffaX+/fsrMjJSBQoUUJs2bcyOBQAA4gAzWQAQx+7cuaN69eqpd+/eioyMlCStXLlShmGYnAwAAMQFShYAxKEDBw6oePHi+uGHH2xjAwYM0P/+9z/ufQUAQBLF6YIAEAcMw9CMGTPUs2dPhYeHS5LSpUunxYsXq1atWianAwAAcYmSBQCxLCQkRB06dNCqVatsY2XKlNHKlSuVM2dOE5MBAID4wOmCABDLevfubVewevbsqR07dlCwAABIJihZABDLRo0apaxZs8rT01Nr167VxIkT5eTkZHYsAAAQTzhdEABiWaZMmfT9998rffr0ypMnj9lxAABAPGMmCwBew/Hjx+Xv76/g4GC78ZIlS1KwAABIpihZAPCKFi9erJIlS2rz5s1q2bKlrFar2ZEAAEACQMkCgBh69OiROnTooFatWunhw4eSpCtXrujWrVsmJwMAAAkBJQsAYuD06dMqU6aM5s6daxtr166dfvvtN2XMmNHEZAAAIKGgZAFANK1cuVK+vr76448/JElubm5auHCh5s6dK1dXV5PTAQCAhILVBQHgJcLCwtSrVy9Nnz7dNlaoUCEFBATozTffNDEZAABIiJjJAoCXWL9+vV3B+vDDD7V//34KFgAAeCZKFgC8RP369dWqVSs5Oztrzpw5WrRokTw8PMyOBQAAEihKFgD8x3+XYrdYLJoxY4b279+v9u3by2KxmJQMAAAkBpQsAPiXy5cvq0KFClq7dq3duLu7u95++22TUgEAgMSEkgUA/9/GjRvl4+OjvXv3qm3btjp//rzZkQAAQCJEyQKQ7EVGRurzzz/Xe++9p9u3b0uS0qRJo5CQEJOTAQCAxIgl3AEka1evXlWzZs20c+dO21idOnW0cOFCpU2b1sRkAAAgsWImC0CytXXrVvn4+NgKlqOjoyZMmKAffviBggUAAF4ZJQtAshMVFaURI0aoWrVqunHjhiQpe/bs2rlzp3r16sXqgQAA4LVQsgAkOzdu3NCUKVNkGIYkqUaNGgoMDFS5cuVMTgYAAJICShaAZCdLlixavHixUqRIoS+++EIbNmxQhgwZzI4FAACSCBa+AJDkWa1WhYWFydXV1TZWq1YtnTlzRrlz5zYvGAAASJKYyQKQpN26dUt16tRR27ZtbacHPkHBAgAAcYGZLABJ1r59+9S4cWP99ddfkqSKFSvqk08+MTkVAABI6pjJApDkGIahyZMn65133rEVrAwZMihv3rwmJwMAAMkBM1kAkpS7d+/qo48+0tq1a21jFSpU0PLly5U9e3YTkwEAgOSCmSwAScahQ4fk6+trV7D69u2rX375hYIFAADiDTNZABI9wzA0e/Zsde/eXeHh4ZKktGnTatGiRapdu7bJ6QAAQHJDyQKQJGzatMlWsEqVKqVVq1YpV65cJqcCAADJEacLAkj0LBaL5s+fL29vb3Xv3l27du2iYAEAANMwkwUg0TEMQ0FBQcqSJYttLG3atAoMDJSnp6eJyQAAAJjJApDIPHjwQG3atFGxYsV09epVu20ULAAAkBBQsgAkGidOnFCpUqW0aNEi3bhxQ82aNZPVajU7FgAAgB1KFoBEYcmSJSpRooSOHz8uSXJ3d1enTp3k4MC3MQAAkLBwTRaABO3Ro0fq3r275syZYxt76623FBAQoIIFC5qYDAAA4NkoWQASrDNnzqhRo0Y6cuSIbaxt27aaNm2a3NzcTEwGAADwfJxnAyBB+u677+Tr62srWK6urpo/f77mzZtHwQIAAAkaM1kAEqSIiAjdv39fkvTGG29o9erVeuutt0xOBQAA8HKULAAJUpMmTbRz507dvXtXs2fPloeHh9mRAAAAooWSBSBBOHLkiIoWLWo3NmXKFDk6OspisZiUCgAAIOa4JguAqSIiItSnTx8VK1ZMS5YssduWIkUKChYAAEh0KFkATPP333+rcuXKmjBhgiSpY8eOunTpksmpAAAAXg8lC4ApNm3apGLFimnPnj2SpJQpU2rMmDHKmTOnyckAAABeDyULQLyKjIzUoEGDVKtWLd26dUuSlCtXLu3evVvdunXj9EAAAJDosfAFgHhz7do1NW/eXNu3b7eN1a5dWwsXLlS6dOnMCwYAABCLmMkCEC9+//13+fj42AqWo6Ojxo0bpx9++IGCBQAAkhRmsgDEixw5cthOBcyaNatWrlypChUqmJwKAAAg9jGTBSBeeHl5afny5apZs6YOHz5MwQIAAEkWJQtAnNizZ49u375tN1a5cmVt3LhRGTNmNCkVAABA3KNkAYhVVqtV48ePV8WKFdW6dWtZrVazIwEAAMQrShaAWHP79m3VrVtXffv2VVRUlNavX69ly5aZHQsAACBesfAFgFixf/9+NW7cWJcuXbKNDRo0SE2bNjUxFQAAQPyjZAF4LYZhaOrUqerdu7ciIiIkSenTp9eSJUtUo0YNk9MBAADEP0oWgFd27949tWvXTmvWrLGNlStXTitWrFCOHDlMTAYAAGAeShaAVxIcHKwyZcro3LlztrHevXtr9OjRSpkypYnJAAAAzMXCFwBeSfr06VW6dGlJUpo0afTDDz9o/PjxFCwAAJDsMZMF4JVYLBbNnj1bkvTFF18od+7c5gYCAABIIChZAKLl6NGjCgoKkp+fn23Mw8NDS5cuNTEVAABAwsPpggBeauHChSpVqpQaN26sixcvmh0HAAAgQaNkAXiuhw8fql27dmrTpo0ePXqkO3fuaMSIEWbHAgAASNA4XRDAM506dUoNGzbU0aNHbWMff/yxJk+ebF4oAACARICZLABPWb58uUqUKGErWG5ublq8eLFmz54tV1dXk9MBAAAkbMxkAbB5/PixPvvsM82aNcs2VrhwYQUEBKhw4cImJgMAAEg8KFkAJEmGYahOnTr6+eefbWOtWrXSjBkz5O7ubmIyAACAxIXTBQFI+ue+V127dpUkubi46Ntvv9WCBQsoWAAAADHETBYAm/fff19fffWVqlatqrffftvsOAAAAIkSM1lAMnXp0iWNGjVKhmHYjX/22WcULAAAgNfATBaQDK1fv16tWrXSnTt3lDFjRnXs2NHsSAAAAEkGM1lAMhIREaF+/fqpTp06unPnjiRpypQpioiIMDkZAABA0kHJApKJK1euqEqVKho3bpxtrF69etqzZ49SpkxpYjIAAICkhZIFJAObN29WsWLFtHv3bklSihQpNGnSJK1Zs0Zp0qQxNxwAAEASQ8kCkrCoqCgNHTpUNWrUUHBwsCQpR44c2rVrl3r06CGLxWJyQgAAgKSHkgUkYSNHjtSIESNsKwjWqlVLgYGBKlOmjMnJAAAAki5KFpCEdevWTTlz5pSjo6O+/PJL/e9//1P69OnNjgUAAJCksYQ7kISlS5dOAQEBevz4sSpWrGh2HAAAgGSBmSwgiQgODlarVq10/fp1u/FSpUpRsAAAAOIRM1lAErBnzx41adJEf//9t65cuaLNmzfL0dHR7FgAAADJEjNZQCJmGIYmTpyoSpUq6e+//5YkHT16VOfPnzc5GQAAQPJFyQISqTt37qhevXrq3bu3IiMjJUkVK1ZUYGCg8ufPb3I6AACA5IuSBSRCv//+u4oXL64ffvjBNjZgwABt3bpVWbNmNTEZAAAAuCYLSEQMw9D06dPVs2dPRURESPpnBcHFixerVq1aJqcDAACARMkCEpWdO3eqa9eutsdly5bVihUrlDNnThNTAQAA4N84XRBIRCpVqqQOHTpIknr16qUdO3ZQsAAAABIYZrKARGbKlClq0KCB/P39zY4CAACAZ2AmC0igQkND1bJlS61cudJu3NXVlYIFAACQgFGygATo2LFjKlWqlJYsWaL27dvr1KlTZkcCAABANFGygARm0aJFKlWqlE6cOGEbO3funImJAAAAEBOULCCBePTokdq3b6/WrVvr4cOHkqQiRYro4MGDLM8OAACQiFCygATg9OnTKlOmjL799lvbWPv27fXbb7+pQIECJiYDAABATFGyAJOtXLlSvr6++uOPPyRJbm5uWrhwoebMmSNXV1eT0wEAACCmWMIdMFFISIi6deum0NBQSVKhQoUUEBCgN9980+RkAAAAeFXMZAEmSp06tZYtWyaLxaIPP/xQ+/fvp2ABAAAkcsxkAfEsKipKjo6OtsdVq1bVwYMHVaxYMVksFhOTAQAAIDYwkwXEk4iICPXq1UuNGjWSYRh223x8fChYAAAASUSCKFnTp09X7ty55eLiotKlS2v//v3P3XfOnDl65513lDZtWqVNm1Z+fn4v3B9ICC5fvqyKFSvqq6++0tq1azVp0iSzIwEAACCOmF6yVq5cqZ49e2ro0KE6dOiQihYtKn9/f924ceOZ+2/fvl3NmjXTtm3btHfvXuXIkUPVq1fXlStX4jk5ED0bN26Uj4+P9u3bJ0lKmTKlXFxcTE4FAACAuGIx/nveUjwrXbq0SpYsqWnTpkmSrFarcuTIoa5du6p///4vfX5UVJTSpk2radOmqVWrVi/dPyQkRJ6enprcYpG6L2n52vmB54mMjNSQIUM0ZswY21ju3Lm1atUqlSxZ0sRkAAAAkP6vG9y7d0+pU6eOtdc1deGL8PBwHTx4UAMGDLCNOTg4yM/PT3v37o3Wazx8+FARERFKly7dM7eHhYUpLCzM9jgkJOT1QgPRcPXqVTVr1kw7d+60jb3//vtasGCB0qZNa2IyAAAAxDVTTxcMDg5WVFSUMmfObDeeOXNmBQUFRes1+vXrp6xZs8rPz++Z28eMGSNPT0/bR44cOV47N/AiW7dulY+Pj61gOTo6asKECfr+++8pWAAAAMlAol7C/csvv9SKFSu0ffv2517jMmDAAPXs2dP2OCQkhKKFODV37lzbNYXZs2fXypUrVa5cOZNTAQDMEhUVpYiICLNjAMmWk5OTHBzid27J1JKVIUMGOTo66vr163bj169fl5eX1wufO2HCBH355Zf6+eef9fbbbz93P2dnZzk7O8dKXiA6Zs+erYMHDypv3rxavHixMmTIYHYkAIAJDMNQUFCQ7t69a3YUIFlzcHCQt7e3nJyc4u2YppYsJycn+fr6auvWrapbt66kfxa+2Lp1qz799NPnPm/cuHH64osv9NNPP6lEiRLxlBZ4tvv37ytVqlS2x6lTp9aOHTuUOXPmeP+tCQAg4XhSsDJlyiQ3NzfuhwiYwGq16urVq7p27Zpy5swZb/8OTT9dsGfPnmrdurVKlCihUqVKafLkyXrw4IHatm0rSWrVqpWyZctmW6Ft7NixGjJkiJYtW6bcuXPbrt3y8PCQh4eHae8DyY/VatXYsWM1efJkHThwwO401CxZspiYDABgtqioKFvBSp8+vdlxgGQtY8aMunr1qiIjI5UyZcp4OabpJatJkya6efOmhgwZoqCgIBUrVkybNm2yLYZx+fJlu9mAmTNnKjw8XA0bNrR7naFDh2rYsGHxGR3J2K1bt9SyZUv9+OOPkqTGjRtrx44d8ToNDQBIuJ5cg+Xm5mZyEgBPfj6LiopKPiVLkj799NPnnh64fft2u8cXL16M+0DAC+zdu1dNmjTRX3/9JUmyWCzy9/eXo6OjyckAAAkNpwgC5jPj32GCKFlAYmAYhiZPnqy+ffsqMjJS0j/Tz0uXLlW1atVMTgcAAICEgpIFRMPdu3fVtm1bff/997axChUqaMWKFcqWLZt5wQAAAJDgsPQZ8BIHDx5U8eLF7QpWv379tG3bNgoWACDZslgsdv9vTGq2bt2qQoUKKSoqyuwoSUpwcLAyZcqkv//+2+wocYqSBbzE5cuXdeHCBUlS2rRptX79en355ZdKkYKJYABA0hQUFKSuXbsqT548cnZ2Vo4cOVSnTh1t3brV7Gg2Z8+eVdu2bZU9e3Y5OzvL29tbzZo104EDB+z227Ztm2rVqqX06dPLzc1NhQsXVq9evXTlypUXvn7fvn01aNCgJHvN9Xfffafq1asrffr0slgsOnz4cLSeFxAQoIIFC8rFxUVFihTRxo0b7bYbhqEhQ4YoS5YscnV1lZ+fn86cOWPbniFDBrVq1UpDhw6NzbeT4FCygJeoV6+ePvvsM5UuXVqBgYF67733zI4EAECcuXjxonx9ffXLL79o/Pjx+vPPP7Vp0ya9++676tKli9nxJEkHDhyQr6+vTp8+rdmzZ+v48eNau3atChYsqF69etn2mz17tvz8/OTl5aU1a9bo+PHjmjVrlu7du6eJEyc+9/V3796tc+fOqUGDBq+VMzw8/LWeH5cePHigChUqaOzYsdF+zp49e9SsWTO1a9dOgYGBqlu3rurWraujR4/a9hk3bpy+/vprzZo1S7/99pvc3d3l7++vx48f2/Zp27atli5dqtu3b8fqe0pQjGTm3r17hiRjcotFZkdBAnXp0iXDarXajYWFhRlhYWEmJQIAJDaPHj0yjh8/bjx69MjsKDFWs2ZNI1u2bEZoaOhT2+7cuWP7syRj7dq1tsd9+/Y18ufPb7i6uhre3t7GoEGDjPDwcNv2w4cPG5UrVzY8PDyMVKlSGcWLFzd+//13wzAM4+LFi0bt2rWNNGnSGG5ubkbhwoWNDRs2PDOf1Wo13nzzTcPX19eIiop6bsa//vrLcHJyMnr06PHM1/n3e/mvLl26GA0bNrQbO3v2rPH+++8bmTJlMtzd3Y0SJUoYW7ZssdsnV65cxogRI4yWLVsaqVKlMlq3bm0YhmHs2rXLqFChguHi4mJkz57d6Nq1q93nd9GiRYavr6/h4eFhZM6c2WjWrJlx/fr15+aLTRcuXDAkGYGBgS/dt3HjxsZ7771nN1a6dGmjY8eOhmH883fj5eVljB8/3rb97t27hrOzs7F8+XK753l7extz5859/TcQDS/69/ikG9y7dy9Wj8n5TsD/ZxiG5s2bp08//VTTp0/XRx99ZNvG/a8AALGhztTdunk/LN6PmzGVs/7XtcJL97t9+7Y2bdqkL774Qu7u7k9tT5MmzXOfmypVKi1YsEBZs2bVn3/+qQ4dOihVqlTq27evJKlFixby8fHRzJkz5ejoqMOHD9vuWdSlSxeFh4dr586dcnd31/Hjx+Xh4fHM4xw+fFjHjh3TsmXL7O6l+t+MAQEBCg8Ptx0/Ju9l165dat68ud1YaGioatWqpS+++ELOzs5atGiR6tSpo1OnTilnzpy2/SZMmKAhQ4bYToc7d+6catSooVGjRmnevHm6efOm7fZF8+fPl/TPfdVGjhypN954Qzdu3FDPnj3Vpk2bp07F+7dOnTppyZIlz93+JHNs2rt3r3r27Gk35u/vb7s278KFCwoKCpKfn59tu6enp0qXLq29e/eqadOmtvFSpUpp165dateuXaxmTCgoWYD+mTL/5JNPtGjRIkn/fLMvXbq03nzzTZOTAQCSkpv3wxQU8vjlO5rk7NmzMgxDBQsWjPFzBw0aZPtz7ty51bt3b61YscJWci5fvqw+ffrYXjt//vy2/S9fvqwGDRqoSJEikqQ8efI89zhPru95WcYzZ84oderUypIlS4zfy6VLl5Q1a1a7saJFi6po0aK2xyNHjtTatWu1bt06u/u9VqlSxe6Uxfbt26tFixbq0aOHpH/e99dff61KlSpp5syZcnFxsfvFbp48efT111+rZMmSCg0NfW7ZHDFihHr37h3j9/Y6goKClDlzZruxzJkzKygoyLb9ydjz9nkia9asCgwMjMO05qJkIdk7ceKEGjZsqOPHj9vG2rZtq7x585qYCgCQFGVM5Zygj2sYxisfY+XKlfr666917tw5hYaGKjIyUqlTp7Zt79mzp9q3b6/FixfLz89PjRo1sv2/tlu3burcubM2b94sPz8/NWjQQG+//fZrZTQM45VvQvvo0SO5uLjYjYWGhmrYsGHasGGDrl27psjISD169EiXL1+2269EiRJ2j48cOaI//vhDS5cutctmtVp14cIFFSpUSAcPHtSwYcN05MgR3blzR1arVdI/5bNw4cLPzJgpUyZlypTpld5fQuDq6qqHDx+aHSPOULKQrC1ZskQdO3a0/SP38PDQN998o2bNmpmcDACQFEXnlD0z5c+fXxaLRSdPnozR8/bu3asWLVpo+PDh8vf3l6enp1asWGG3uMSwYcPUvHlzbdiwQT/++KOGDh2qFStWqF69emrfvr38/f21YcMGbd68WWPGjNHEiRPVtWvXp45VoEABSdLJkyfl4+Pz3EwFChTQvXv3dO3atRjPZmXIkEF37tyxG+vdu7e2bNmiCRMmKF++fHJ1dVXDhg2fWtziv6dZhoaGqmPHjurWrdtTx8mZM6cePHggf39/+fv7a+nSpcqYMaMuX74sf3//Fy6cYcbpgl5eXrp+/brd2PXr1+Xl5WXb/mTs35/z69evq1ixYnbPu337tjJmzBir+RISVhdEsvTo0SN9/PHHatmypa1gFSlSRAcOHKBgAQCSrXTp0snf31/Tp0/XgwcPntp+9+7dZz5vz549ypUrlwYOHKgSJUoof/78unTp0lP7FShQQJ999pk2b96s+vXr265JkqQcOXKoU6dO+u6779SrVy/NmTPnmccqVqyYChcurIkTJ9pmfJ6VsWHDhnJyctK4ceOe+TrPey+S5OPjY3eGiyT9+uuvatOmjerVq6ciRYrIy8tLFy9efO5rPFG8eHEdP35c+fLle+rDyclJJ0+e1K1bt/Tll1/qnXfeUcGCBXXjxo2Xvu6IESN0+PDhF37EtrJlyz61jP+WLVtUtmxZSZK3t7e8vLzs9gkJCdFvv/1m2+eJo0ePvrAkJ3bMZCHZuXjxourVq2f3zeejjz7S1KlT5ebmZl4wAAASgOnTp6t8+fIqVaqURowYobfffluRkZHasmWLZs6cqRMnTjz1nPz58+vy5ctasWKFSpYsqQ0bNmjt2rW27Y8ePVKfPn3UsGFDeXt76++//9bvv/9uWyK9R48eqlmzpgoUKKA7d+5o27ZtKlSo0DPzWSwWzZ8/X35+fnrnnXc0cOBAFSxYUKGhofrf//6nzZs3a8eOHcqRI4cmTZqkTz/9VCEhIWrVqpVy586tv//+W4sWLZKHh8dzl3H39/fXwoULn3qP3333nerUqSOLxaLBgwc/s+T9V79+/VSmTBl9+umnat++vW1hjy1btmjatGnKmTOnnJycNHXqVHXq1ElHjx7VyJEjX/q6r3u64O3bt3X58mVdvXpVknTq1ClJ/8xGPZmRatWqlbJly6YxY8ZIkrp3765KlSpp4sSJeu+997RixQodOHBA33zzjaR//m569OihUaNGKX/+/PL29tbgwYOVNWtW1a1b13bshw8f6uDBgxo9evQr50/wYnWtwkSAJdxx8+ZNI1u2bIYkw9XV1Zg/f77ZkQAASUxiXsLdMAzj6tWrRpcuXYxcuXIZTk5ORrZs2Yz333/f2LZtm20f/WcJ9z59+hjp06c3PDw8jCZNmhiTJk0yPD09DcP451YoTZs2NXLkyGE4OTkZWbNmNT799FPb5+fTTz818ubNazg7OxsZM2Y0WrZsaQQHB78w46lTp4xWrVoZWbNmNZycnIxcuXIZzZo1Mw4dOmS335YtWwx/f38jbdq0houLi1GwYEGjd+/extWrV5/72rdu3TJcXFyMkydP2sYuXLhgvPvuu4arq6uRI0cOY9q0aUalSpWM7t272/bJlSuXMWnSpKdeb//+/Ua1atUMDw8Pw93d3Xj77beNL774wrZ92bJlRu7cuQ1nZ2ejbNmyxrp166K9rPqrmj9/viHpqY+hQ4fa9qlUqZJtGfonVq1aZRQoUMBwcnIy3nzzzaeW2rdarcbgwYONzJkzG87OzkbVqlWNU6dO2e2zbNky44033oirt/YUM5ZwtxjGa1zhmAiFhITI09NTk1ssUvclLc2OA5Ps2bNHHTt21PLly/XWW2+ZHQcAkMQ8fvxYFy5ckLe391MLKCBx6NOnj0JCQjR79myzoyQ5ZcqUUbdu3Z5aJj+uvOjf45NucO/ePbuFWl4X12Qhybt48aJu3rxpN1auXDkdOXKEggUAAJ5p4MCBypUrV7ROCUT0BQcHq379+kn+GnhKFpK0devWycfHRx9++KGioqLstj3rBoYAAADSPzcr/vzzz/l5IZZlyJBBffv2feXl9RMLvmqQJEVERKhPnz764IMPdPfuXW3evFlTp041OxYAAACSAVYXRJLz999/q0mTJtqzZ49trEGDBmrbtq2JqQAAAJBcMJOFJGXTpk0qVqyYrWClTJlSX3/9tQICAuTp6WlyOgAAACQHlCwkCZGRkRo0aJBq1aqlW7duSZJy5cql3bt3q2vXrkn+vF8AAAAkHJwuiETv0aNHqlWrlrZv324bq127thYuXKh06dKZFwwAAADJEjNZSPRcXV3l7e0tSXJ0dNS4ceP0ww8/ULAAAABgCkoWkoRp06apRo0a2rFjh/r06cNyqwAAxDGLxaLvv//e7BhxYuvWrSpUqNBTt3/B6wkODlamTJn0999/mx0lzvGTKBKdmzdvaseOHXZjbm5u+vHHH1W+fHmTUgEAkHQEBQWpa9euypMnj5ydnZUjRw7VqVNHW7duNTuaJKly5cqyWCxPfXTq1Mluv23btqlWrVpKnz693NzcVLhwYfXq1UtXrlx54ev37dtXgwYNkqOjY1y+DdN89913ql69utKnTy+LxaLDhw9H63kBAQEqWLCgXFxcVKRIEW3cuNFuu2EYGjJkiLJkySJXV1f5+fnpzJkztu0ZMmRQq1atNHTo0Nh8OwkSJQuJyu7du+Xj46P3339fZ8+eNTsOAABJzsWLF+Xr66tffvlF48eP159//qlNmzbp3XffVZcuXcyOZ9OhQwddu3bN7mPcuHG27bNnz5afn5+8vLy0Zs0aHT9+XLNmzdK9e/c0ceLE577u7t27de7cOTVo0OC18oWHh7/W8+PSgwcPVKFCBY0dOzbaz9mzZ4+aNWumdu3aKTAwUHXr1lXdunV19OhR2z7jxo3T119/rVmzZum3336Tu7u7/P399fjxY9s+bdu21dKlS3X79u1YfU8JjpHM3Lt3z5BkTG6xyOwoiIGoqChj3LhxhqOjoyHJkGTUqFHD7FgAADzTo0ePjOPHjxuPHj0yO0qM1axZ08iWLZsRGhr61LY7d+7Y/izJWLt2re1x3759jfz58xuurq6Gt7e3MWjQICM8PNy2/fDhw0blypUNDw8PI1WqVEbx4sWN33//3TAMw7h48aJRu3ZtI02aNIabm5tRuHBhY8OGDc/NWKlSJaN79+7P3f7XX38ZTk5ORo8ePZ65/d/v47+6dOliNGzY0G7s7Nmzxvvvv29kypTJcHd3N0qUKGFs2bLFbp9cuXIZI0aMMFq2bGmkSpXKaN26tWEYhrFr1y6jQoUKhouLi5E9e3aja9eudp/bRYsWGb6+voaHh4eROXNmo1mzZsb169efmy82XbhwwZBkBAYGvnTfxo0bG++9957dWOnSpY2OHTsahmEYVqvV8PLyMsaPH2/bfvfuXcPZ2dlYvny53fO8vb2NuXPnvv4biKYX/Xt80g3u3bsXq8dkJgsJ3u3bt/XBBx+ob9++tnOjK1eurPnz55ucDACApOX27dvatGmTunTpInd396e2p0mT5rnPTZUqlRYsWKDjx49rypQpmjNnjiZNmmTb3qJFC2XPnl2///67Dh48qP79+ytlypSSpC5duigsLEw7d+7Un3/+qbFjx8rDw+OV30dAQIDCw8PVt2/fZ25/0fvYtWuXSpQoYTcWGhqqWrVqaevWrQoMDFSNGjVUp04dXb582W6/CRMmqGjRogoMDNTgwYN17tw51ahRQw0aNNAff/yhlStXavfu3fr0009tz4mIiNDIkSN15MgRff/997p48aLatGnzwvfXqVMneXh4vPAjtu3du1d+fn52Y/7+/tq7d68k6cKFCwoKCrLbx9PTU6VLl7bt80SpUqW0a9euWM+YkLCEOxK03377TY0bN7b7JjZo0CANHTpUKVLw5QsASGRmV5JCb8T/cT0ySR13vHS3s2fPyjAMFSxYMMaHGDRokO3PuXPnVu/evbVixQpb0bl8+bL69Olje+38+fPb9r98+bIaNGigIkWKSJLy5Mnz0uPNmDFDc+fOtRubPXu2WrRooTNnzih16tTKkiVLjN/HpUuXlDVrVruxokWLqmjRorbHI0eO1Nq1a7Vu3Tq7wlSlShX16tXL9rh9+/Zq0aKFevToIemf9/z111+rUqVKmjlzplxcXPTRRx/Z9s+TJ4++/vprlSxZUqGhoc8tSyNGjFDv3r1j/N5eR1BQkDJnzmw3ljlzZgUFBdm2Pxl73j5PZM2aVYGBgXGY1nz8lIoEyTAMTZ06Vb1791ZERIQkKX369FqyZIlq1KhhcjoAAF5R6A3p/lWzUzyXYRiv/NyVK1fq66+/1rlz5xQaGqrIyEilTp3atr1nz55q3769Fi9eLD8/PzVq1Eh58+aVJHXr1k2dO3fW5s2b5efnpwYNGujtt99+4fFatGihgQMH2o09+QHfMAxZLJZXeh+PHj2Si4uL3VhoaKiGDRumDRs26Nq1a4qMjNSjR4+emsn67wzYkSNH9Mcff2jp0qW2McMwZLVadeHCBRUqVEgHDx7UsGHDdOTIEd25c0dWq1XSP8WzcOHCz8yYKVMmZcqU6ZXeX0Lg6uqqhw8fmh0jTlGykCB17txZs2fPtj0uV66cVqxYoRw5cpiYCgCA1+Rh0g/G0Txu/vz5ZbFYdPLkyRi9/N69e9WiRQsNHz5c/v7+8vT01IoVK/5fe3ceV2P6/w/81XbaF6nUSVpUhKQiYoxl4jCWEBUNWUIp2bNFzCCDrBMzlikzmMLg46FRsk2phizZiiylGcS0KK2nOtf3D7/u3xxtSjqV9/PxOI9H57rf932/r+Nu5ry7rvu6xRaYWLNmDSZNmoSIiAicPXsWAQEBCAsLw9ixY+Hh4QGBQICIiAicO3cOgYGBCAoKwty5c2s8p7q6OkxNTavdZm5ujry8PLx8+bLeo1laWlrIzc0Va1u8eDGio6OxZcsWmJqaQlFREePHj6+yuMX7UywLCgowe/Zs+Pr6VjlPhw4dUFhYCIFAAIFAgMOHD0NbWxsZGRkQCAS1Lpzh6emJQ4cO1dqPgoKCurpaL7q6unj16pVY26tXr6Crq8ttr2z772f+6tUr9OjRQ2y/nJwcaGtrN2p+zQ0VWaRZGjNmDPbu3QvGGJYsWYL169dz87YJIYSQFusDpuxJkqamJgQCAYKDg+Hr61ulaHjz5k219zPFx8fD0NBQbGTp2bNnVeLMzc1hbm6OBQsWYOLEiQgJCcHYsWMBAAYGBvD09ISnpyeWL1+Offv21Vpk1Wb8+PFYtmwZNm3aJHZfWF39AABra2skJyeLtcXFxWHq1KlcrgUFBUhPT68zDxsbGyQnJ9dYDN69exfZ2dnYuHEj94fk69ev13lcSUwXtLe3x4ULF7ipjwAQHR0Ne3t7AICxsTF0dXVx4cIFrqjKz8/H1atX4eXlJXase/fuYeDAgU2UuWRQkUWapWHDhiEwMBAWFhYYPXq0pNMhhBBCPhvBwcHo168f7Ozs8O2336J79+4oLy9HdHQ09uzZg5SUlCr7mJmZISMjA2FhYejVqxciIiJw8uRJbntxcTGWLFmC8ePHw9jYGP/88w8SExO5ZdLnz5+P4cOHw9zcHLm5ubh06RIsLCxqzbOoqKjKvT7y8vJo06YNDAwMsG3bNvj4+CA/Px9TpkyBkZER/vnnH/zyyy9QUVGpcRl3gUCAgwcPVunfiRMnMGrUKEhJSWHVqlXctL7aLF26FH369IGPjw88PDygrKyM5ORkREdH44cffkCHDh3A4/Gwa9cueHp64t69e/juu+/qPO7HThfMyclBRkYGXrx4N3X14cOHAN6NRlWOSE2ZMgX6+voIDAwEAMybNw8DBgxAUFAQRowYgbCwMFy/fh179+4F8O7h1PPnz8e6detgZmYGY2NjrFq1Cnw+H2PGjOHOXVRUhBs3bmDDhg0Nzr9FaNS1ClsAWsK9+cnPz2c7duxgIpFI0qkQQgghjaIlL+HOGGMvXrxg3t7ezNDQkPF4PKavr89Gjx7NLl26xMXgvSXclyxZwtq2bctUVFSYi4sL27ZtG1NXV2eMMVZaWspcXV2ZgYEB4/F4jM/nMx8fH+7z8fHxYR07dmTy8vJMW1ubTZ48mWVlZdWY34ABA7hHuvz3JRAIxOKio6OZQCBgbdq0YQoKCqxz585s8eLF7MWLFzUeOzs7mykoKLAHDx5wbWlpaWzQoEFMUVGRGRgYsB9++KHKMvKGhoZs27ZtVY537do1NmTIEKaiosKUlZVZ9+7d2fr167ntR44cYUZGRkxeXp7Z29uz06dPf/Cy6g0VEhJS7ecXEBDAxQwYMIBbhr7S0aNHmbm5OePxeKxr165VltkXiURs1apVrF27dkxeXp599dVX7OHDh2IxR44cYZ06dfpUXauWJJZwl2LsI+5wbIHy8/Ohrq6O7W6/YN6hyZJO57N39+5djB8/HqmpqdixY0e1c5YJIYSQlqakpARpaWkwNjausogCaf6WLFmC/Px8sfvDSePo06cPfH19MWnSpCY7Z22/j5W1QV5enthCLR+LnpNFJCYkJAS9e/dGamoqgHfzi9++fSvhrAghhBDyuVu5ciUMDQ0/aEog+XBZWVkYN24cJk6cKOlUPjkqskiTKyoqwrRp0zB9+nQUFxcDAHr06IG//voLqqqqEs6OEEIIIZ87DQ0NrFixAtLS9FW5MWlpacHPz6/By+u3JHTlkCb14MED9O7dG6GhoVzb7NmzkZCQUOPKO4QQQgghhLQkVGSRJnPkyBH07NkT9+7dA/DuWRKHDh3Cjz/+SPPVCSGEEEJIq0FLuJMmceDAAXh4eHDvu3btiuPHj6Nz584SzIoQQgghhJDGRyNZpEmMHz8eJiYmAAB3d3dcvXqVCixCCCGEENIq0UgWaRLq6uo4duwYkpKSMH36dEmnQwghhBBCyCdDI1mk0QmFQqxYsQLPnz8Xa7exsaECixBCCCGEtHo0kkUa1bNnz+Ds7Ixr164hNjYWFy9ehJycnKTTIoQQQgghpMnQSBZpNGfOnIG1tTWuXbsGALh27RquX78u4awIIYQQ8ilISUnh1KlTkk6jxXj48CF0dXXx9u1bSafSqgiFQhgZGTW775xUZJGPVlZWhqVLl2LUqFHIzc0FAJiYmCAhIQH29vYSzo4QQggh9ZWZmYm5c+fCxMQE8vLyMDAwwKhRo3DhwgVJpwYAGDhwIObPn1+lPTQ0FBoaGmJtQqEQmzdvho2NDZSVlaGurg4rKyv4+/vjxYsXXNy///4LLy8vdOjQAfLy8tDV1YVAIEBcXJzY8W7duoUJEyagXbt2UFBQgJmZGWbOnInU1NRac16+fDnmzp0LVVXVBve7OYuJicGoUaPA5/PrVYBfvnwZNjY2kJeXh6mpqdizVCsFBwfDyMgICgoK6N27N/cHfQDg8XhYvHgxli5d2kg9aRxUZJGP8vz5cwwePBibNm3i2saOHYsbN27AxsZGgpkRQgghpCHS09Nha2uLixcvYvPmzbh79y4iIyMxaNAgeHt7Szq9eiktLcWQIUOwYcMGTJ06FTExMbh79y527tyJrKws7Nq1i4t1cnLCrVu3cPDgQaSmpuL06dMYOHAgsrOzuZgzZ86gT58+KC0txeHDh5GSkoJDhw5BXV0dq1atqjGPjIwMnDlzBlOnTv2o/giFwo/a/1MqLCyElZUVgoODP3iftLQ0jBgxAoMGDUJSUhLmz58PDw8PREVFcTHh4eFYuHAhAgICcPPmTVhZWUEgEOD169dcjJubG65cuYL79+83ap8+CvvM5OXlMQBsu9svkk6lxYuKimJaWloMAAPAZGVl2bZt25hIJJJ0aoQQQohEFRcXs+TkZFZcXCzpVOpt+PDhTF9fnxUUFFTZlpuby/0MgJ08eZJ77+fnx8zMzJiioiIzNjZm/v7+TCgUctuTkpLYwIEDmYqKClNVVWU2NjYsMTGRMcZYeno6GzlyJNPQ0GBKSkqsS5cuLCIiosYcBwwYwObNm1elPSQkhKmrq3PvAwMDmbS0NLt582a1x6n8zpKbm8sAsMuXL9d4zsLCQqalpcXGjBlT7fb/fjbv27x5M+vZs6dYW1ZWFnN1dWV8Pp8pKiqybt26sSNHjojFDBgwgHl7e7N58+axtm3bsoEDBzLGGLt79y4bNmwYU1ZWZjo6Ouybb75h//77L7ff2bNnWb9+/Zi6ujrT1NRkI0aMYI8fP64xv8b2/rVREz8/P9a1a1exNhcXFyYQCLj3dnZ2zNvbm3tfUVHB+Hw+CwwMFNtv0KBBzN/fv9rz1Pb7WFkb5OXl1ZlvfdDCF6RBUlJSMGzYMDDGAAAdOnRAeHg4+vTpI+HMCCGEkObL5YwLsoqzmvy8WopaCB8ZXmdcTk4OIiMjsX79eigrK1fZ/v5UvP9SVVVFaGgo+Hw+7t69i5kzZ0JVVRV+fn4A3o02WFtbY8+ePZCRkUFSUhK3OJa3tzeEQiFiYmKgrKyM5ORkqKioNKyz//Hbb79hyJAhsLa2rna7lJQUAEBFRQUqKio4deoU+vTpA3l5+SqxUVFRyMrK4vrzvto+m9jYWPTs2VOsraSkBLa2tli6dCnU1NQQERGByZMno2PHjrCzs+PiDh48CC8vL27a4ps3bzB48GB4eHhg27ZtKC4uxtKlS+Hs7IyLFy8CeDeqtHDhQnTv3h0FBQVYvXo1xo4di6SkJEhLVz+RbcOGDdiwYUONfQCA5ORkdOjQodaY+khISICDg4NYm0Ag4KaCCoVC3LhxA8uXL+e2S0tLw8HBAQkJCWL72dnZITY2ttFy+1hUZJEGsbCwgI+PD3bt2oURI0bg4MGDaNu2raTTIoQQQpq1rOIsvC56XXeghDx+/BiMMXTu3Lne+/r7+3M/GxkZYfHixQgLC+OKkoyMDCxZsoQ7tpmZGRefkZEBJycnWFpaAnh3b3dddu/ejf3794u1lZeXQ0FBgXufmpqKgQMHisWMHTsW0dHRAIDu3bsjPj4esrKyCA0NxcyZM/Hjjz/CxsYGAwYMgKurK7p37w4AePToEQA06LN59uxZlSJLX18fixcv5t7PnTsXUVFROHr0qFiRZWZmJnZbxrp162BtbS1WEP38888wMDBAamoqzM3N4eTkJHaun3/+Gdra2khOTka3bt2qzdHT0xPOzs619oPP59fd2XrIzMxEu3btxNratWuH/Px8FBcXIzc3FxUVFdXGPHjwoEpuz549a9T8PgYVWaTBNm/ejB49emDq1Kk1/lWEEEIIIf+flqJWsz5v5QyVhggPD8fOnTvx5MkTFBQUoLy8HGpqatz2hQsXwsPDA7/++iscHBwwYcIEdOzYEQDg6+sLLy8vnDt3Dg4ODnBycuKKm5q4ublh5cqVYm0nTpyoczRm9+7dKCwsxM6dOxETE8O1Ozk5YcSIEYiNjcVff/2Fs2fPYtOmTdi/fz+mTp36UZ9NcXGxWPEHABUVFdiwYQOOHj2K58+fQygUorS0FEpKSmJxtra2Yu9v376NS5cuVTvS9+TJE5ibm+PRo0dYvXo1rl69iqysLIhEIgDvitmaiixNTU1oamo2uI+SpqioiKKiIkmnwaEii9RJJBIhMDAQBgYGmDJlCtcuLy9PDxcmhBBC6uFDpuxJkpmZGaSkpKqMEtQlISEBbm5uWLt2LQQCAdTV1REWFoagoCAuZs2aNZg0aRIiIiJw9uxZBAQEICwsDGPHjoWHhwcEAgEiIiJw7tw5BAYGIigoCHPnzq3xnOrq6jA1NRVr09HRqdKfhw8firXp6ekBQLUFhYKCAoYMGYIhQ4Zg1apV8PDwQEBAAKZOnQpzc3MAwIMHD+q9erKWlha3AnOlzZs3Y8eOHdi+fTssLS2hrKyM+fPnV1nc4v1pmwUFBRg1ahS+//77Kuep7NuoUaNgaGiIffv2gc/nQyQSoVu3brUunCGJ6YK6urp49eqVWNurV6+gpqYGRUVFyMjIQEZGptoYXV1dsbacnBxoa2s3Wm4fi4YfSK2ysrLw9ddfw9/fH56enrh3756kUyKEEELIJ6KpqQmBQIDg4GAUFhZW2f7mzZtq94uPj4ehoSFWrlyJnj17wszMrNqpW+bm5liwYAHOnTuHcePGISQkhNtmYGAAT09PnDhxAosWLcK+ffs+uj8TJ05EdHQ0bt261aD9u3Tpwn0OQ4cOhZaWltjUvf+q6bMBAGtrayQnJ4u1xcXFwdHREd988w2srKxgYmJS5zLwAGBjY4P79+/DyMgIpqamYi9lZWVkZ2fj4cOH8Pf3x1dffQULC4sqBV51PD09kZSUVOursacL2tvbV3ksQHR0NFfE8ng82NraisWIRCJcuHChSqF77969Gu+9kwQqskiN4uLi0KNHD24ZzZKSkirPiiCEEEJI6xIcHIyKigrY2dnh999/x6NHj5CSkoKdO3fWOIJjZmaGjIwMhIWF4cmTJ9i5cydOnjzJbS8uLoaPjw8uX76MZ8+eIS4uDomJibCwsAAAzJ8/H1FRUUhLS8PNmzdx6dIlbtvHWLBgAezt7fHVV19hx44duHnzJtLS0hAVFYWzZ89CRkYGAJCdnY3Bgwfj0KFDuHPnDtLS0nDs2DFs2rQJjo6OAN6NKO3fvx8REREYPXo0zp8/j/T0dFy/fh1+fn7w9PSsMQ+BQICEhARUVFSIfWbR0dGIj49HSkoKZs+eXWXEpjre3t7IycnBxIkTkZiYiCdPniAqKgrTpk1DRUUF2rRpg7Zt22Lv3r14/PgxLl68iIULF9Z5XE1NzSpF2/svWdmaJ8EVFBRwxRjwbnn2pKQkZGRkcDHLly8XmxXl6emJp0+fws/PDw8ePMDu3btx9OhRLFiwgItZuHAh9u3bh4MHDyIlJQVeXl4oLCzEtGnTxM4fGxuLoUOH1tnPJtOoaxW2ALSEe91EIhHbvHkzk5GR4ZZn19HRYefPn5d0aoQQQkiL0JKXcGeMsRcvXjBvb29maGjIeDwe09fXZ6NHj2aXLl3iYvDeMt1Llixhbdu2ZSoqKszFxYVt27aNW069tLSUubq6MgMDA8bj8Rifz2c+Pj7c5+Pj48M6duzI5OXlmba2Nps8eTLLysqqMb8PXcKdMcZKSkrYxo0bmZWVFVNUVGTy8vKsc+fObMGCBSwjI4OLWbZsGbOxsWHq6upMSUmJderUifn7+7OioiKx4yUmJrJx48YxbW1tJi8vz0xNTdmsWbPYo0ePasy3rKyM8fl8FhkZybVlZ2czR0dHpqKiwnR0dJi/vz+bMmUKc3R0rLOfqampbOzYsUxDQ4MpKiqyzp07s/nz53NL0kdHRzMLCwsmLy/Punfvzi5fvvzBy6o31KVLl7jvjf99ubu7czHu7u5swIABVfbr0aMH4/F4zMTEhIWEhFQ59q5du1iHDh0Yj8djdnZ27K+//hLbHh8fzzQ0NKr8W1WSxBLuUox9xF18LVB+fj7U1dWx3e0XzDs0WdLpNDu5ubmYOnUqTp8+zbV9+eWX+O233xp9iJgQQghprUpKSpCWlgZjY+MqCx6Qz1NwcDBOnz4t9qBd0jhcXFxgZWWFFStWVLu9tt/HytogLy9PbKGWj0ULXxBOYmIinJ2dkZ6ezrUtX74c3377ba3Dw4QQQgghpHazZ8/Gmzdv8PbtW6iqqko6nVZDKBTC0tJSbIphc0DfnAkAoKysTKzA0tTUxKFDhzB8+HDJJkYIIYQQ0grIyspWWXKefDwejyf2jLbmgha+IAAAOTk5HDx4EDIyMrC3t0dSUhIVWIQQQgghhDQAjWR9xhhjkJKS4t5/+eWXOHfuHPr37w85OTkJZkYIIYQQQkjLRSNZnyHGGPbt24fx48dzTwCvNHjwYCqwCCGEEEII+QhUZH1mCgoKMGXKFMyaNQsnTpyo9mnhhBBCCCGEkIaj6YKfkfv372PChAlISUnh2jIzM6tMGySEEEIIIYQ0HI1kfSZ++eUX2NnZcQWWqqoqwsLCsGPHDiqwCCGEEEIIaUQ0ktXKFRcXY+7cuThw4ADX1r17dxw7dgzm5uYSzIwQQgghhJDWiUayWrHU1FT06dNHrMDy8PDAX3/9RQUWIYQQQj6KlJQUTp06Jek0mr0vv/wSR44ckXQarc6yZcswd+5cSadRIyqyWrGgoCDcuXMHAKCkpISDBw9i3759UFRUlHBmhBBCCGnOMjMzMXfuXJiYmEBeXh4GBgYYNWoULly4IOnUAAADBw7E/Pnza425dOkSRo4cCW1tbSgoKKBjx45wcXFBTEyMWNy+fftgZWUFFRUVaGhowNraGoGBgWIx+fn5WLlyJTp37gwFBQXo6urCwcEBJ06cAGOsxhxOnz6NV69ewdXVtcF9bc5KSkowdepUWFpaQlZWFmPGjPmg/XJycuDm5gY1NTVoaGhgxowZKCgoEIu5c+cO+vfvDwUFBRgYGGDTpk1i2xcvXoyDBw/i6dOnjdWdRkVFViu2detWdOnSBRYWFrh27RqmTJki6ZQIIYQQ0sylp6fD1tYWFy9exObNm3H37l1ERkZi0KBB8Pb2lnR6H2T37t346quv0LZtW4SHh+Phw4c4efIk+vbtiwULFnBxP//8M+bPnw9fX18kJSUhLi4Ofn5+Yl/437x5g759++KXX37B8uXLcfPmTcTExMDFxQV+fn7Iy8urMY+dO3di2rRpkJZu+FfuioqKKo/caS4qKiqgqKgIX19fODg4fPB+bm5uuH//PqKjo3HmzBnExMRg1qxZ3Pb8/HwMHToUhoaGuHHjBjZv3ow1a9Zg7969XIyWlhYEAgH27NnTqH1qNOwzk5eXxwCw7W6/SDqVRicUCqu0paWlsbdv30ogG0IIIeTzVVxczJKTk1lxcbGkU6m34cOHM319fVZQUFBlW25uLvczAHby5EnuvZ+fHzMzM2OKiorM2NiY+fv7i303SUpKYgMHDmQqKipMVVWV2djYsMTERMYYY+np6WzkyJFMQ0ODKSkpsS5durCIiIgacxwwYACbN29etduePXvG5OTk2IIFC6rdLhKJuJ8dHR3Z1KlTazwPY4x5eXkxZWVl9vz58yrb3r59y8rKyqrd7/Xr10xKSordu3dPrD0oKIh169aNKSkpsfbt2zMvLy+x72ohISFMXV2d/e9//2MWFhZMRkaGpaWlsZKSErZo0SLG5/OZkpISs7OzY5cuXeL2y8rKYq6urozP5zNFRUXWrVs3duTIkVr71pjc3d2Zo6NjnXHJyckMAPdvzxhjZ8+eZVJSUtxnvHv3btamTRtWWlrKxSxdupR16tRJ7FgHDx5k7du3r/Octf0+VtYGeXl5dR6nPmjhi1bi5MmTWLBgAS5evAgTExOu3cjISHJJEUIIIURMmtN4lGdlNfl5ZbW0YPz78TrjcnJyEBkZifXr10NZWbnKdg0NjRr3VVVVRWhoKPh8Pu7evYuZM2dCVVUVfn5+AN6NXlhbW2PPnj2QkZFBUlIS5OTkAADe3t4QCoWIiYmBsrIykpOToaKi0qC+/v777ygrK+PO+77/rqqsq6uLP//8E8+ePYOhoWGVWJFIhLCwMLi5uYHP51fZXluOV65cgZKSEiwsLMTapaWlsXPnThgbG+Pp06eYM2cO/Pz8sHv3bi6mqKgI33//Pfbv34+2bdtCR0cHPj4+SE5ORlhYGPh8Pk6ePIlhw4bh7t27MDMzQ0lJCWxtbbF06VKoqakhIiICkydPRseOHWFnZ1dtjhkZGejSpUuNfQCAFStWYMWKFbXG1EdCQgI0NDTQs2dPrs3BwQHS0tK4evUqxo4di4SEBHz55Zfg8XhcjEAgwPfff4/c3Fy0adMGAGBnZ4d//vkH6enpze47LxVZLZxQKMTSpUuxfft2AICzszOuXLkCBQUFySZGCCGEkCrKs7JQ/uqVpNOo0ePHj8EYQ+fOneu9r7+/P/ezkZERFi9ejLCwMK7YycjIwJIlS7hjm5mZcfEZGRlwcnKCpaUlAIj9wbi+UlNToaamBl1dXa7t999/h7u7O/c+ISEBlpaWCAgIwLhx42BkZARzc3PY29vj66+/xvjx4yEtLY2srCzk5uY26PN49uwZ2rVrV2Wq4H/vJTMyMsK6devg6ekpVmSVlZVh9+7dsLKyAvDu8wkJCUFGRgZX7C1evBiRkZEICQnBhg0boK+vj8WLF3PHmDt3LqKionD06NEaiyw+n4+kpKRa+6GpqVmfbtcpMzMTOjo6Ym2ysrLQ1NREZmYmF2NsbCwW065dO25bZZFV+Vk8e/aMiizSeDIyMuDs7IyrV69ybR07dkR5ebkEsyKEEEJITWS1tJr1eVktizjUJTw8HDt37sSTJ09QUFCA8vJyqKmpcdsXLlwIDw8P/Prrr3BwcMCECRPQsWNHAICvry+8vLxw7tw5ODg4wMnJCd27d29wLu8/A1QgECApKQnPnz/HwIEDUVFRAQDQ09NDQkIC7t27h5iYGMTHx8Pd3R379+9HZGTkR30excXF1f7R+/z58wgMDMSDBw+Qn5+P8vJylJSUoKioCEpKSgAAHo8n1v+7d++ioqKiyurQpaWlaNu2LYB390dt2LABR48exfPnzyEUClFaWsodszqysrIwNTVtcB8lrXIxt6KiIglnUhUVWS1UREQEpkyZgpycHADvfhm3bdsGLy8vergwIYQQ0kx9yJQ9STIzM4OUlBQePHhQr/0SEhLg5uaGtWvXQiAQQF1dHWFhYQgKCuJi1qxZg0mTJiEiIgJnz55FQEAAwsLCMHbsWHh4eEAgECAiIgLnzp1DYGAggoKCGrREt5mZGfLy8pCZmcmNZqmoqMDU1BSystV/9e3WrRu6deuGOXPmwNPTE/3798eff/6JAQMGQENDo96fB/BuYYbc3FyxtvT0dIwcORJeXl5Yv349NDU1ceXKFcyYMQNCoZAriBQVFcW+zxUUFEBGRgY3btyAjIyM2DErpyxu3rwZO3bswPbt22FpaQllZWXMnz8fQqGwxhwlMV1QV1cXr1+/FmsrLy9HTk4O9++lq6uLV++N+Fa+/+8IZeX3YG1t7UbLr7HQ6oItTHl5OZYvX46RI0dyF5aRkRHi4uIwZ84cKrAIIYQQ0mCampoQCAQIDg5GYWFhle1v3rypdr/4+HgYGhpi5cqV6NmzJ8zMzPDs2bMqcebm5liwYAHOnTuHcePGISQkhNtmYGAAT09PnDhxAosWLcK+ffsa1Ifx48dDTk4O33//fYP2ryw6CgsLIS0tDVdXVxw+fBgvXryoEls5Ylcda2trZGZmihVaN27cgEgkQlBQEPr06QNzc/Nqj1vdsSoqKvD69WuYmpqKvSqLjri4ODg6OuKbb76BlZUVTExMkJqaWutxK6cL1vby9PSsM7/6sLe3x5s3b3Djxg2u7eLFixCJROjduzcXExMTg7KyMi4mOjoanTp14qYKAsC9e/cgJyeHrl27NmqOjYFGslqQFy9ewNXVFbGxsVybo6MjQkJCxC44QgghhJCGCg4ORr9+/WBnZ4dvv/0W3bt3R3l5OaKjo7Fnzx6kpKRU2cfMzAwZGRkICwtDr169EBERgZMnT3Lbi4uLsWTJEowfPx7Gxsb4559/kJiYCCcnJwDv7lMaPnw4zM3NkZubi0uXLlVZMOJ9//77b5X7ifT09NChQwcEBQVh3rx5yMnJwdSpU2FsbIycnBwcOnQIALjRIC8vL/D5fAwePBjt27fHy5cvsW7dOmhra8Pe3h4AsH79ely+fBm9e/fG+vXr0bNnT8jJySE2NhaBgYFITEysdkEQa2traGlpIS4uDiNHjgQAmJqaoqysDLt27cKoUaMQFxeHH3/8sc5/E3Nzc7i5uWHKlCkICgqCtbU1/v33X1y4cAHdu3fHiBEjYGZmhuPHjyM+Ph5t2rTB1q1b8erVq1pHqhpjumBycjKEQiFycnLw9u1b7t+kR48eAMA9RujChQvQ19eHhYUFhg0bhpkzZ+LHH39EWVkZfHx84Orqyt1jNWnSJKxduxYzZszA0qVLce/ePezYsQPbtm0TO3dsbCz69+/fPJ8B26hrFbYALXkJ97NnzzIADACTlZVlW7duFVuGlBBCCCHNQ0tewp0xxl68eMG8vb2ZoaEh4/F4TF9fn40ePVpsyXC8t4T7kiVLWNu2bZmKigpzcXFh27ZtY+rq6owxxkpLS5mrqyszMDBgPB6P8fl85uPjw30+Pj4+rGPHjkxeXp5pa2uzyZMns6ysrBrzGzBgAPed6L+v7777jouJjo5mw4cPZ5qamkxWVpa1a9eOjRkzhkVGRnIxx48fZ19//TXT09Pj8nJycmJ37twRO9+bN2/YsmXLmJmZGePxeKxdu3bMwcGBnTx5stbvYn5+fszV1VWsbevWrUxPT48pKioygUDAfvnlFwaAWx6/cgn39wmFQrZ69WpmZGTE5OTkmJ6eHhs7diyXa3Z2NnN0dGQqKipMR0eH+fv7sylTpnzQsuofw9DQsNp/i0qXLl1iAFhaWhrXlp2dzSZOnMhUVFSYmpoamzZtWpVHDt2+fZt98cUXTF5enunr67ONGzdWOXenTp3Yb7/9VmeOkljCXYqxj7ijrwXKz8+Huro6trv9gnmHJks6nXpbvnw5Dh06hKNHj3J/YSGEEEJI81JSUoK0tDQYGxvTir+fsczMTHTt2hU3b96sdol40nBnz57FokWLcOfOnRrvtatU2+9jZW2Ql5cntlDLx6J7spqxN2/eVFnV5rvvvsPt27epwCKEEEIIaeZ0dXVx4MABZGRkSDqVVqewsBAhISF1FliSQkVWM/Xnn3/CwsKiyjzdyucIEEIIIYSQ5m/MmDHo37+/pNNodcaPH88tlNEcUZHVzIhEIgQGBmLw4MHIzMzE/PnzxVZfIYQQQgghhDRvzXN87TOVnZ2NyZMn4+zZs1xb//790b59ewlmRQghhBBCCKkPGslqJhISEmBtbc0VWFJSUggICEBUVBTatWsn4ewIIYQQQgghH4pGsiSMMYbt27fDz8+Pe5idtrY2Dh8+jCFDhkg4O0IIIYQQQkh9UZElQW/evMG0adNw6tQprq1///4ICwvjHsZGCCGEEEIIaVlouqAEMcbEnlS+bNkyXLx4kQosQgghhBBCWjAqsiSoTZs2OHr0KHR1dXHmzBkEBgY227X+CSGEEEIIIR+GiqwmlJ+fj9evX4u19erVC2lpaRgxYoSEsiKEEEIIqT8pKSmxWx5aq+zsbOjo6CA9PV3SqbQ6ffr0we+//y7pND4JKrKayO3bt9GzZ084OztzC1xUUlBQkFBWhBBCCCFVZWZmYu7cuTAxMYG8vDwMDAwwatQoXLhwQdKpAQAGDhwIKSkpbNy4scq2ESNGQEpKCmvWrBFrf/z4MaZNm4b27dtDXl4exsbGmDhxIq5fv17rudavXw9HR0cYGRk1Yg+aj/v378PJyQlGRkaQkpLC9u3bP2i/O3fuoH///lBQUICBgQE2bdpUJebYsWPo3LkzFBQUYGlpiT/++ENsu7+/P5YtWwaRSNQYXWlWqMj6xBhjOHDgAPr06YNHjx7hzz//xLp16ySdFiGEEEJItdLT02Fra4uLFy9i8+bNuHv3LiIjIzFo0CB4e3tLOj2OgYEBQkNDxdqeP3+OCxcuQE9PT6z9+vXrsLW1RWpqKn766SckJyfj5MmT6Ny5MxYtWlTjOYqKinDgwAHMmDHjo3IVCoUftf+nVFRUBBMTE2zcuBG6uroftE9+fj6GDh0KQ0ND3LhxA5s3b8aaNWuwd+9eLiY+Ph4TJ07EjBkzcOvWLYwZMwZjxozBvXv3uJjhw4fj7du3Ys+IbTXYZyYvL48BYNvdfvnk5yooKGBTpkxhALiXjY0Ne/z48Sc/NyGEEEIkp7i4mCUnJ7Pi4mJJp1Jvw4cPZ/r6+qygoKDKttzcXO5nAOzkyZPcez8/P2ZmZsYUFRWZsbEx8/f3Z0KhkNuelJTEBg4cyFRUVJiqqiqzsbFhiYmJjDHG0tPT2ciRI5mGhgZTUlJiXbp0YRERETXmOGDAAObl5cXatm3Lrly5wrWvX7+ejRo1illZWbGAgADGGGMikYh17dqV2drasoqKilr79L5jx44xbW1tsbby8nI2ffp0ZmRkxBQUFJi5uTnbvn27WIy7uztzdHRk69atY3p6eszIyIgxxlhGRgabMGECU1dXZ23atGGjR49maWlp3H7Xrl1jDg4OrG3btkxNTY19+eWX7MaNGzXm19gMDQ3Ztm3b6ozbvXs3a9OmDSstLeXali5dyjp16sS9d3Z2ZiNGjBDbr3fv3mz27NlibdOmTWPffPPNxyVeh9p+Hytrg7y8vEY9J62y8ImkpKRg/PjxSE5O5tq8vLywdetWmh5ICCGEfKaObkhEUX7Tj2ooqfHgvKJXnXE5OTmIjIzE+vXroaysXGW7hoZGjfuqqqoiNDQUfD4fd+/excyZM6Gqqgo/Pz8AgJubG6ytrbFnzx7IyMggKSkJcnJyAABvb28IhULExMRAWVkZycnJUFFRqTVXHo8HNzc3hISEoF+/fgCA0NBQbNq0SWyqYFJSEu7fv48jR45AWrrqJK7a+hQbGwtbW1uxNpFIhPbt2+PYsWNo27Yt4uPjMWvWLOjp6cHZ2ZmLu3DhAtTU1BAdHQ0AKCsrg0AggL29PWJjYyErK4t169Zh2LBhuHPnDng8Ht6+fQt3d3fs2rULjDEEBQXh66+/xqNHj6CqqlptjocPH8bs2bNr/azOnj2L/v371xpTHwkJCfjyyy/B4/G4NoFAgO+//x65ublo06YNEhISsHDhQrH9BAJBlfv47Ozsqp322dJRkfUJHDp0CLNnz0ZRUREAQEVFBfv27YOrq6uEMyOEEEKIJBXlC1H4plTSadTo8ePHYIyhc+fO9d7X39+f+9nIyAiLFy9GWFgYV2RlZGRgyZIl3LHNzMy4+IyMDDg5OcHS0hIAYGJi8kHnnD59Ovr3748dO3bgxo0byMvLw8iRI8WKrEePHgFAg/r07NmzKo/WkZOTw9q1a7n3xsbGSEhIwNGjR8WKLGVlZezfv58rRA4dOgSRSIT9+/dDSkoKABASEgINDQ1cvnwZQ4cOxeDBg8XOtXfvXmhoaODPP//EyJEjq81x9OjR6N27d6390NfX//BOf4DMzEwYGxuLtbVr147b1qZNG2RmZnJt/43JzMwUa+Pz+fj7778hEomqLYJbKiqyGpFIJIKXl5fYfFRLS0scO3YMnTp1kmBmhBBCCGkOlNR4dQdJ8LyMsQafIzw8HDt37sSTJ09QUFCA8vJyqKmpcdsXLlwIDw8P/Prrr3BwcMCECRPQsWNHAICvry+8vLxw7tw5ODg4wMnJCd27d6/znFZWVjAzM8Px48dx6dIlTJ48ucrjcD6mT8XFxdXOQAoODsbPP/+MjIwMFBcXQygUokePHmIxlpaWYiM9t2/fxuPHj6uMSJWUlODJkycAgFevXsHf3x+XL1/G69evUVFRgaKiImRkZNSYo6qqao2jXC2BoqIiRCIRSktLoaioKOl0Gg0VWY1IWlparAKfPn06du3aBSUlJQlmRQghhJDm4kOm7EmSmZkZpKSk8ODBg3rtl5CQADc3N6xduxYCgQDq6uoICwtDUFAQF7NmzRpMmjQJEREROHv2LAICAhAWFoaxY8fCw8MDAoEAEREROHfuHAIDAxEUFIS5c+fWee7p06cjODgYycnJuHbtWpXt5ubmAIAHDx7A2tq6Xv3S0tJCbm6uWFtYWBgWL16MoKAg2NvbQ1VVFZs3b8bVq1fF4t6fbllQUABbW1scPny4ynm0tbUBAO7u7sjOzsaOHTtgaGgIeXl52Nvb17pwhiSmC+rq6uLVq1dibZXvKxfPqCnm/cU1cnJyoKys3KoKLIBWF2x027ZtwxdffIHQ0FAcOHCACixCCCGEtBiampoQCAQIDg5GYWFhle1v3rypdr/4+HgYGhpi5cqV6NmzJ8zMzPDs2bMqcebm5liwYAHOnTuHcePGISQkhNtmYGAAT09PnDhxAosWLcK+ffs+KOdJkybh7t276NatG7p06VJle48ePdClSxcEBQVVu1R4TX0CAGtra7H76wEgLi4Offv2xZw5c2BtbQ1TU1NuJKo2NjY2ePToEXR0dGBqair2UldX547t6+uLr7/+Gl27doW8vDyysrJqPe7o0aORlJRU66tnz5515lcf9vb2iImJQVlZGdcWHR2NTp06oU2bNlzM+0v+R0dHw97eXqzt3r179S5+WwIqsj5CaWlplb+YKCgoICYmBu7u7hLKihBCCCGk4YKDg1FRUQE7Ozv8/vvvePToEVJSUrBz584qX5ArmZmZISMjA2FhYXjy5Al27tyJkydPctuLi4vh4+ODy5cv49mzZ4iLi0NiYiIsLCwAAPPnz0dUVBTS0tJw8+ZNXLp0idtWlzZt2uDly5c1PsNLSkoKISEhSE1NRf/+/fHHH3/g6dOnuHPnDvcMrJoIBALcv39fbDTLzMwM169fR1RUFFJTU7Fq1SokJibWmaebmxu0tLTg6OiI2NhYpKWl4fLly/D19cU///zDHfvXX39FSkoKrl69Cjc3tzpHeFRVVasUbe+/ajuGUCjkijGhUIjnz58jKSkJjx8/5mJ++OEHfPXVV9z7SZMmgcfjYcaMGbh//z7Cw8OxY8cOsYUu5s2bh8jISAQFBeHBgwdYs2YNrl+/Dh8fH7Hzx8bGYujQoXV+fi0NFVkNlJaWhi+++AKDBw+uMqReeTMjIYQQQkhLY2Jigps3b2LQoEFYtGgRunXrhiFDhuDChQvYs2dPtfuMHj0aCxYsgI+PD3r06IH4+HisWrWK2y4jI4Ps7GxMmTIF5ubmcHZ2xvDhw7kFJCoqKuDt7Q0LCwsMGzYM5ubm2L179wfnrKGhUe1qiJXs7Oxw/fp1mJqaYubMmbCwsMDo0aNx//79Wh++a2lpCRsbGxw9epRrmz17NsaNGwcXFxf07t0b2dnZmDNnTp05KikpISYmBh06dMC4ceNgYWGBGTNmoKSkhLt37cCBA8jNzYWNjQ0mT54MX19f6OjofPDn0BAvXryAtbU1rK2t8fLlS2zZsgXW1tbw8PDgYrKyssRG69TV1XHu3DmkpaXB1tYWixYtwurVqzFr1iwupm/fvjhy5Aj27t0LKysrHD9+HKdOnUK3bt24mOfPnyM+Ph7Tpk37pH2UBCn2MXcDtkD5+flQV1fHdrdfMO/Q5AYd4/Tp03B3d+eGl21tbZGYmEjFFSGEEEIAvFvMIC0tDcbGxvTolhYuIiICS5Yswb1791rV6nfNwdKlS5Gbmyu2aNynUNvvY2VtkJeXJ7ZQy8eihS/qoaysDCtWrMCWLVu4to4dO2Lfvn1UYBFCCCGEtEIjRozAo0eP8Pz5cxgYGEg6nVZFR0enyrO0Wgsqsj7Q33//DVdXV8THx3NtTk5OOHDgAHezIiGEEEIIaX3mz58v6RRapUWLFkk6hU+Gxjw/QGRkJKytrbkCS05ODjt37sSxY8eowCKEEEIIIYSIoZGsOmzZsgVLlizh3hsaGuLo0aOws7OTYFaEEEIIIYSQ5opGsupga2vL3eQ4atQo3Lx5kwosQgghhHyQz2x9MUKaJUn8HtJIVh0GDRqE9evXQ1ZWFosWLaIFLgghhBBSJzk5OQBAUVFRnc85IoR8WkKhEMC7Rwk0FSqy/qOiogLh4eFwdXUVW6Jz2bJlEsyKEEIIIS2NjIwMNDQ08Pr1awDvnpFEf6glpOmJRCL8+++/UFJSgqxs05U+VGT9P69fv8Y333yD6OhoPH/+XOw+LEIIIYSQ+tLV1QUArtAihEiGtLQ0OnTo0KR/6KAiC0BsbCxcXV3x4sULAIC/vz/c3NzA5/MlnBkhhBBCWiopKSno6elBR0cHZWVlkk6HkM8Wj8dr8gdJf9ZFlkgkwubNm7Fy5UpUVFQAePdXpyNHjlCBRQghhJBGISMj06T3ghBCJK9ZrC4YHBwMIyMjKCgooHfv3rh27Vqt8ceOHUPnzp2hoKAAS0tL/PHHH/U+Z2FpAUaPHo1ly5ZxBdagQYNw69YtDBo0qEH9IIQQQgghhBCJF1nh4eFYuHAhAgICcPPmTVhZWUEgENQ4fzk+Ph4TJ07EjBkzcOvWLYwZMwZjxozBvXv36nXeLX/4IyIiAsC74fxVq1YhOjqamz9NCCGEEEIIIQ0hxST8AIfevXujV69e+OGHHwC8m8JnYGCAuXPnVruqn4uLCwoLC3HmzBmurU+fPujRowd+/PHHOs+Xn58PdXV17r2WlhYOHToEgUDQCL0hhBBCCCGEtBSVtUFeXh7U1NQa7bgSvSdLKBTixo0bWL58OdcmLS0NBwcHJCQkVLtPQkICFi5cKNYmEAhw6tSpauNLS0tRWlrKvc/Ly+N+7t27N0JCQqCvr4/8/PyP6AkhhBBCCCGkpamsARp73EmiRVZWVhYqKirQrl07sfZ27drhwYMH1e6TmZlZbXxmZma18YGBgVi7dm21265evYouXbo0IHNCCCGEEEJIa5GdnS022+1jtfrVBZcvXy428vXmzRsYGhoiIyOjUT9IQt6Xn58PAwMD/P333406/EzI++haI02FrjXSVOhaI00lLy8PHTp0gKamZqMeV6JFlpaWFmRkZPDq1Sux9levXtW4AIWurm694uXl5SEvL1+lXV1dnX5pSZNQU1Oja400CbrWSFOha400FbrWSFNp7OdoSXR1QR6PB1tbW1y4cIFrE4lEuHDhAuzt7avdx97eXiweAKKjo2uMJ4QQQgghhJCmJPHpggsXLoS7uzt69uwJOzs7bN++HYWFhZg2bRoAYMqUKdDX10dgYCAAYN68eRgwYACCgoIwYsQIhIWF4fr169i7d68ku0EIIYQQQgghAJpBkeXi4oJ///0Xq1evRmZmJnr06IHIyEhucYuMjAyx4bu+ffviyJEj8Pf3x4oVK2BmZoZTp06hW7duH3Q+eXl5BAQEVDuFkJDGRNcaaSp0rZGmQtcaaSp0rZGm8qmuNYk/J4sQQgghhBBCWhOJ3pNFCCGEEEIIIa0NFVmEEEIIIYQQ0oioyCKEEEIIIYSQRkRFFiGEEEIIIYQ0olZZZAUHB8PIyAgKCgro3bs3rl27Vmv8sWPH0LlzZygoKMDS0hJ//PFHE2VKWrr6XGv79u1D//790aZNG7Rp0wYODg51XpuEVKrvf9cqhYWFQUpKCmPGjPm0CZJWo77X2ps3b+Dt7Q09PT3Iy8vD3Nyc/j9KPkh9r7Xt27ejU6dOUFRUhIGBARYsWICSkpImypa0VDExMRg1ahT4fD6kpKRw6tSpOve5fPkybGxsIC8vD1NTU4SGhtb7vK2uyAoPD8fChQsREBCAmzdvwsrKCgKBAK9fv642Pj4+HhMnTsSMGTNw69YtjBkzBmPGjMG9e/eaOHPS0tT3Wrt8+TImTpyIS5cuISEhAQYGBhg6dCieP3/exJmTlqa+11ql9PR0LF68GP3792+iTElLV99rTSgUYsiQIUhPT8fx48fx8OFD7Nu3D/r6+k2cOWlp6nutHTlyBMuWLUNAQABSUlJw4MABhIeHY8WKFU2cOWlpCgsLYWVlheDg4A+KT0tLw4gRIzBo0CAkJSVh/vz58PDwQFRUVP1OzFoZOzs75u3tzb2vqKhgfD6fBQYGVhvv7OzMRowYIdbWu3dvNnv27E+aJ2n56nutva+8vJypqqqygwcPfqoUSSvRkGutvLyc9e3bl+3fv5+5u7szR0fHJsiUtHT1vdb27NnDTExMmFAobKoUSStR32vN29ubDR48WKxt4cKFrF+/fp80T9K6AGAnT56sNcbPz4917dpVrM3FxYUJBIJ6natVjWQJhULcuHEDDg4OXJu0tDQcHByQkJBQ7T4JCQli8QAgEAhqjCcEaNi19r6ioiKUlZVBU1PzU6VJWoGGXmvffvstdHR0MGPGjKZIk7QCDbnWTp8+DXt7e3h7e6Ndu3bo1q0bNmzYgIqKiqZKm7RADbnW+vbtixs3bnBTCp8+fYo//vgDX3/9dZPkTD4fjVUbyDZmUpKWlZWFiooKtGvXTqy9Xbt2ePDgQbX7ZGZmVhufmZn5yfIkLV9DrrX3LV26FHw+v8ovMiH/1ZBr7cqVKzhw4ACSkpKaIEPSWjTkWnv69CkuXrwINzc3/PHHH3j8+DHmzJmDsrIyBAQENEXapAVqyLU2adIkZGVl4YsvvgBjDOXl5fD09KTpgqTR1VQb5Ofno7i4GIqKih90nFY1kkVIS7Fx40aEhYXh5MmTUFBQkHQ6pBV5+/YtJk+ejH379kFLS0vS6ZBWTiQSQUdHB3v37oWtrS1cXFywcuVK/Pjjj5JOjbQyly9fxoYNG7B7927cvHkTJ06cQEREBL777jtJp0ZItVrVSJaWlhZkZGTw6tUrsfZXr15BV1e32n10dXXrFU8I0LBrrdKWLVuwceNGnD9/Ht27d/+UaZJWoL7X2pMnT5Ceno5Ro0ZxbSKRCAAgKyuLhw8fomPHjp82adIiNeS/a3p6epCTk4OMjAzXZmFhgczMTAiFQvB4vE+aM2mZGnKtrVq1CpMnT4aHhwcAwNLSEoWFhZg1axZWrlwJaWkaNyCNo6baQE1N7YNHsYBWNpLF4/Fga2uLCxcucG0ikQgXLlyAvb19tfvY29uLxQNAdHR0jfGEAA271gBg06ZN+O677xAZGYmePXs2Raqkhavvtda5c2fcvXsXSUlJ3Gv06NHcKkkGBgZNmT5pQRry37V+/frh8ePHXCEPAKmpqdDT06MCi9SoIddaUVFRlUKqsrh/t54BIY2j0WqD+q3J0fyFhYUxeXl5FhoaypKTk9msWbOYhoYGy8zMZIwxNnnyZLZs2TIuPi4ujsnKyrItW7awlJQUFhAQwOTk5Njdu3cl1QXSQtT3Wtu4cSPj8Xjs+PHj7OXLl9zr7du3kuoCaSHqe629j1YXJB+qvtdaRkYGU1VVZT4+Puzhw4fszJkzTEdHh61bt05SXSAtRH2vtYCAAKaqqsp+++039vTpU3bu3DnWsWNH5uzsLKkukBbi7du37NatW+zWrVsMANu6dSu7desWe/bsGWOMsWXLlrHJkydz8U+fPmVKSkpsyZIlLCUlhQUHBzMZGRkWGRlZr/O2uiKLMcZ27drFOnTowHg8HrOzs2N//fUXt23AgAHM3d1dLP7o0aPM3Nyc8Xg81rVrVxYREdHEGZOWqj7XmqGhIQNQ5RUQEND0iZMWp77/XfsvKrJIfdT3WouPj2e9e/dm8vLyzMTEhK1fv56Vl5c3cdakJarPtVZWVsbWrFnDOnbsyBQUFJiBgQGbM2cOy83NbfrESYty6dKlar9/VV5f7u7ubMCAAVX26dGjB+PxeMzExISFhITU+7xSjNEYKyGEEEIIIYQ0llZ1TxYhhBBCCCGESBoVWYQQQgghhBDSiKjIIoQQQgghhJBGREUWIYQQQgghhDQiKrIIIYQQQgghpBFRkUUIIYQQQgghjYiKLEIIIYQQQghpRFRkEUIIIYQQQkgjoiKLEEJIg4SGhkJDQ0PSaTSYlJQUTp06VWvM1KlTMWbMmCbJhxBCSOtBRRYhhHzGpk6dCikpqSqvx48fSzo1hIaGcvlIS0ujffv2mDZtGl6/ft0ox3/58iWGDx8OAEhPT4eUlBSSkpLEYnbs2IHQ0NBGOV9N1qxZw/VTRkYGBgYGmDVrFnJycup1HCoICSGk+ZCVdAKEEEIka9iwYQgJCRFr09bWllA24tTU1PDw4UOIRCLcvn0b06ZNw4sXLxAVFfXRx9bV1a0zRl1d/aPP8yG6du2K8+fPo6KiAikpKZg+fTry8vIQHh7eJOcnhBDSuGgkixBCPnPy8vLQ1dUVe8nIyGDr1q2wtLSEsrIyDAwMMGfOHBQUFNR4nNu3b2PQoEFQVVWFmpoabG1tcf36dW77lStX0L9/fygqKsLAwAC+vr4oLCysNTcpKSno6uqCz+dj+PDh8PX1xfnz51FcXAyRSIRvv/0W7du3h7y8PHr06IHIyEhuX6FQCB8fH+jp6UFBQQGGhoYIDAwUO3bldEFjY2MAgLW1NaSkpDBw4EAA4qNDe/fuBZ/Ph0gkEsvR0dER06dP597/73//g42NDRQUFGBiYoK1a9eivLy81n7KyspCV1cX+vr6cHBwwIQJExAdHc1tr6iowIwZM2BsbAxFRUV06tQJO3bs4LavWbMGBw8exP/+9z9uVOzy5csAgL///hvOzs7Q0NCApqYmHB0dkZ6eXms+hBBCPg4VWYQQQqolLS2NnTt34v79+zh48CAuXrwIPz+/GuPd3NzQvn17JCYm4saNG1i2bBnk5OQAAE+ePMGwYcPg5OSEO3fuIDw8HFeuXIGPj0+9clJUVIRIJEJ5eTl27NiBoKAgbNmyBXfu3IFAIMDo0aPx6NEjAMDOnTtx+vRpHD16FA8fPsThw4dhZGRU7XGvXbsGADh//jxevnyJEydOVImZMGECsrOzcenSJa4tJycHkZGRcHNzAwDExsZiypQpmDdvHpKTk/HTTz8hNDQU69ev/+A+pqenIyoqCjwej2sTiURo3749jh07huTkZKxevRorVqzA0aNHAQCLFy+Gs7Mzhg0bhpcvX+Lly5fo27cvysrKIBAIoKqqitjYWMTFxUFFRQXDhg2DUCj84JwIIYTUEyOEEPLZcnd3ZzIyMkxZWZl7jR8/vtrYY8eOsbZt23LvQ0JCmLq6OvdeVVWVhYaGVrvvjBkz2KxZs8TaYmNjmbS0NCsuLq52n/ePn5qayszNzVnPnj0ZY4zx+Xy2fv16sX169erF5syZwxhjbO7cuWzw4MFMJBJVe3wA7OTJk4wxxtLS0hgAduvWLbEYd3d35ujoyL13dHRk06dP597/9NNPjM/ns4qKCsYYY1999RXbsGGD2DF+/fVXpqenV20OjDEWEBDApKWlmbKyMlNQUGAAGAC2devWGvdhjDFvb2/m5ORUY66V5+7UqZPYZ1BaWsoUFRVZVFRUrccnhBDScHRPFiGEfOYGDRqEPXv2cO+VlZUBvBvVCQwMxIMHD5Cfn4/y8nKUlJSgqKgISkpKVY6zcOFCeHh44Ndff+WmvHXs2BHAu6mEd+7cweHDh7l4xhhEIhHS0tJgYWFRbW55eXlQUVGBSCRCSUkJvvjiC+zfvx/5+fl48eIF+vXrJxbfr18/3L59G8C7qX5DhgxBp06dMGzYMIwcORJDhw79qM/Kzc0NM2fOxO7duyEvL4/Dhw/D1dUV0tLSXD/j4uLERq4qKipq/dwAoFOnTjh9+jRKSkpw6NAhJCUlYe7cuWIxwcHB+Pnnn5GRkYHi4mIIhUL06NGj1nxv376Nx48fQ1VVVay9pKQET548acAnQAgh5ENQkUUIIZ85ZWVlmJqairWlp6dj5MiR8PLywvr166GpqYkrV65gxowZEAqF1RYLa9aswaRJkxAREYGzZ88iICAAYWFhGDt2LAoKCjB79mz4+vpW2a9Dhw415qaqqoqbN29CWloaenp6UFRUBADk5+fX2S8bGxukpaXh7NmzOH/+PJydneHg4IDjx4/XuW9NRo0aBcYYIiIi0KtXL8TGxmLbtm3c9oKCAqxduxbjxo2rsq+CgkKNx+XxeNy/wcaNGzFixAisXbsW3333HQAgLCwMixcvRlBQEOzt7aGqqorNmzfj6tWrteZbUFAAW1tbseK2UnNZ3IQQQlojKrIIIYRUcePGDYhEIgQFBXGjNJX3/9TG3Nwc5ubmWLBgASZOnIiQkBCMHTsWNjY2SE5OrlLM1UVaWrrafdTU1MDn8xEXF4cBAwZw7XFxcbCzsxOLc3FxgYuLC8aPH49hw4YhJycHmpqaYservP+poqKi1nwUFBQwbtw4HD58GI8fP0anTp1gY2PDbbexscHDhw/r3c/3+fv7Y/DgwfDy8uL62bdvX8yZM4eLeX8kisfjVcnfxsYG4eHh0NHRgZqa2kflRAgh5MPRwheEEEKqMDU1RVlZGXbt2oWnT5/i119/xY8//lhjfHFxMXx8fHD58mU8e/YMcXFxSExM5KYBLl26FPHx8fDx8UFSUhIePXqE//3vf/Ve+OK/lixZgu+//x7h4eF4+PAhli1bhqSkJMybNw8AsHXrVvz222948OABUlNTcezYMejq6lb7AGUdHR0oKioiMjISr169Ql5eXo3ndXNzQ0REBH7++WduwYtKq1evxi+//IK1a9fi/v37SElJQVhYGPz9/evVN3t7e3Tv3h0bNmwAAJiZmeH69euIiopCamoqVq1ahcTERLF9jIyMcOfOHTx8+BBZWVkoKyuDm5sbtLS04OjoiNjYWKSlpeHy5cvw9fXFP//8U6+cCCGEfDgqsgghhFRhZWWFrVu34vvvv0e3bt1w+PBhseXP3ycjI4Ps7GxMmTIF5ubmcHZ2xvDhw7F27VoAQPfu3fHnn38iNTUV/fv3h7W1NVavXg0+n9/gHH19fbFw4UIsWrQIlpaWiIyMxOnTp2FmZgbg3VTDTZs2oWfPnujVqxfS09Pxxx9/cCNz/yUrK4udO3fip59+Ap/Ph6OjY43nHTx4MDQ1NfHw4UNMmjRJbJtAIMCZM2dw7tw59OrVC3369MG2bdtgaGhY7/4tWLAA+/fvx99//43Zs2dj3LhxcHFxQe/evZGdnS02qgUAM2fORKdOndCzZ09oa2sjLi4OSkpKiImJQYcOHTBu3DhYWFhgxowZKCkpoZEtQgj5hKQYY0zSSRBCCCGEEEJIa0EjWYQQQgghhBDSiKjIIoQQQgghhJBGREUWIYQQQgghhDQiKrIIIYQQQgghpBFRkUUIIYQQQgghjYiKLEIIIYQQQghpRFRkEUIIIYQQQkgjoiKLEEIIIYQQQhoRFVmEEEIIIYQQ0oioyCKEEEIIIYSQRkRFFiGEEEIIIYQ0ov8Dr1augOolVWYAAAAASUVORK5CYII="},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}},{"name":"stdout","text":"Overall Accuracy: 99.08%\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":1},{"cell_type":"code","source":"import warnings\nwarnings.filterwarnings(\"ignore\")\n\nfrom statsmodels.tools.sm_exceptions import ConvergenceWarning\nwarnings.simplefilter(\"ignore\", ConvergenceWarning)\n\nimport pandas as pd\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt\nfrom matplotlib.image import imread\n%matplotlib inline\nimport seaborn as sns\n\nfrom sklearn.metrics import classification_report , confusion_matrix , accuracy_score , auc\nfrom sklearn.model_selection import train_test_split\n\nimport cv2\n#from google.colab.patches import cv2_imshow\nfrom PIL import Image \nimport tensorflow as tf\nfrom tensorflow import keras\nfrom keras import Sequential\nfrom keras.layers import Input, Dense,Conv2D , MaxPooling2D, Flatten,BatchNormalization,Dropout\nfrom tensorflow.keras.preprocessing import image_dataset_from_directory\nimport tensorflow_hub as hub \n\nfrom keras.applications.vgg19 import VGG19\n\nimport joblib","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.51618Z","iopub.execute_input":"2025-02-28T06:15:27.516517Z","iopub.status.idle":"2025-02-28T06:15:27.526607Z","shell.execute_reply.started":"2025-02-28T06:15:27.516489Z","shell.execute_reply":"2025-02-28T06:15:27.525636Z"}},"outputs":[],"execution_count":3},{"cell_type":"markdown","source":"### Import Training Data","metadata":{"toc-hr-collapsed":true}},{"cell_type":"code","source":"learn = pd.read_csv('/kaggle/input/UBC-OCEAN/train.csv')\ndata = learn.copy()","metadata":{"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.52783Z","iopub.execute_input":"2025-02-28T06:15:27.528157Z","iopub.status.idle":"2025-02-28T06:15:27.559333Z","shell.execute_reply.started":"2025-02-28T06:15:27.528129Z","shell.execute_reply":"2025-02-28T06:15:27.557995Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"data.shape","metadata":{"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.562537Z","iopub.execute_input":"2025-02-28T06:15:27.563033Z","iopub.status.idle":"2025-02-28T06:15:27.571396Z","shell.execute_reply.started":"2025-02-28T06:15:27.562988Z","shell.execute_reply":"2025-02-28T06:15:27.570197Z"}},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"(538, 5)"},"metadata":{}}],"execution_count":5},{"cell_type":"markdown","source":"# Model 1\nVGG-16 based model.","metadata":{}},{"cell_type":"code","source":"model1_train_data = data[data[\"is_tma\"]==False]\nmodel1_train_data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.572714Z","iopub.execute_input":"2025-02-28T06:15:27.573118Z","iopub.status.idle":"2025-02-28T06:15:27.601722Z","shell.execute_reply.started":"2025-02-28T06:15:27.573087Z","shell.execute_reply":"2025-02-28T06:15:27.600419Z"}},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":" image_id label image_width image_height is_tma\n0 4 HGSC 23785 20008 False\n1 66 LGSC 48871 48195 False\n3 281 LGSC 42309 15545 False\n4 286 EC 37204 30020 False\n5 431 HGSC 39991 40943 False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idlabelimage_widthimage_heightis_tma
04HGSC2378520008False
166LGSC4887148195False
3281LGSC4230915545False
4286EC3720430020False
5431HGSC3999140943False
\n
"},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"class_labels = ['CC', 'EC', 'HGSC', 'LGSC', 'MC']\nmodel1_train_data['label'] = model1_train_data['label'].replace({'CC':0, 'EC':1, 'HGSC':2, 'LGSC':3, 'MC':4})\nmodel1_train_data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.603097Z","iopub.execute_input":"2025-02-28T06:15:27.603442Z","iopub.status.idle":"2025-02-28T06:15:27.619645Z","shell.execute_reply.started":"2025-02-28T06:15:27.603414Z","shell.execute_reply":"2025-02-28T06:15:27.61821Z"}},"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":" image_id label image_width image_height is_tma\n0 4 2 23785 20008 False\n1 66 3 48871 48195 False\n3 281 3 42309 15545 False\n4 286 1 37204 30020 False\n5 431 2 39991 40943 False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idlabelimage_widthimage_heightis_tma
0422378520008False
16634887148195False
328134230915545False
428613720430020False
543123999140943False
\n
"},"metadata":{}}],"execution_count":7},{"cell_type":"code","source":"model1_test_data = data[data[\"is_tma\"]==True]\nmodel1_test_data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.621068Z","iopub.execute_input":"2025-02-28T06:15:27.621442Z","iopub.status.idle":"2025-02-28T06:15:27.643676Z","shell.execute_reply.started":"2025-02-28T06:15:27.621413Z","shell.execute_reply":"2025-02-28T06:15:27.642333Z"}},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":" image_id label image_width image_height is_tma\n2 91 HGSC 3388 3388 True\n37 4134 MC 2964 2964 True\n76 8280 HGSC 2964 2964 True\n83 9200 MC 3388 3388 True\n112 13568 LGSC 2964 2964 True","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idlabelimage_widthimage_heightis_tma
291HGSC33883388True
374134MC29642964True
768280HGSC29642964True
839200MC33883388True
11213568LGSC29642964True
\n
"},"metadata":{}}],"execution_count":8},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class_labels = ['CC', 'EC', 'HGSC', 'LGSC', 'MC']\nmodel1_test_data['label'] = model1_test_data['label'].replace({'CC':0, 'EC':1, 'HGSC':2, 'LGSC':3, 'MC':4})\nmodel1_test_data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.645051Z","iopub.execute_input":"2025-02-28T06:15:27.645488Z","iopub.status.idle":"2025-02-28T06:15:27.667507Z","shell.execute_reply.started":"2025-02-28T06:15:27.645447Z","shell.execute_reply":"2025-02-28T06:15:27.66623Z"}},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":" image_id label image_width image_height is_tma\n2 91 2 3388 3388 True\n37 4134 4 2964 2964 True\n76 8280 2 2964 2964 True\n83 9200 4 3388 3388 True\n112 13568 3 2964 2964 True","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idlabelimage_widthimage_heightis_tma
291233883388True
374134429642964True
768280229642964True
839200433883388True
11213568329642964True
\n
"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"tma_total_count = len(data[data[\"is_tma\"]==True])\ntma_total_count","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.669224Z","iopub.execute_input":"2025-02-28T06:15:27.669945Z","iopub.status.idle":"2025-02-28T06:15:27.689373Z","shell.execute_reply.started":"2025-02-28T06:15:27.669903Z","shell.execute_reply":"2025-02-28T06:15:27.688009Z"}},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"25"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"model1_test_data[0:12]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.69399Z","iopub.execute_input":"2025-02-28T06:15:27.694392Z","iopub.status.idle":"2025-02-28T06:15:27.709593Z","shell.execute_reply.started":"2025-02-28T06:15:27.694356Z","shell.execute_reply":"2025-02-28T06:15:27.708245Z"}},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":" image_id label image_width image_height is_tma\n2 91 2 3388 3388 True\n37 4134 4 2964 2964 True\n76 8280 2 2964 2964 True\n83 9200 4 3388 3388 True\n112 13568 3 2964 2964 True\n149 17637 2 2964 2964 True\n176 21020 4 3388 3388 True\n236 29084 3 3388 3388 True\n263 31594 1 3388 3388 True\n288 35565 4 2964 2964 True\n299 36302 0 3388 3388 True\n302 36583 3 3388 3388 True","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idlabelimage_widthimage_heightis_tma
291233883388True
374134429642964True
768280229642964True
839200433883388True
11213568329642964True
14917637229642964True
17621020433883388True
23629084333883388True
26331594133883388True
28835565429642964True
29936302033883388True
30236583333883388True
\n
"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"wsi_total_count = len(data[data[\"is_tma\"]==False])\nwsi_total_count","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.711039Z","iopub.execute_input":"2025-02-28T06:15:27.711447Z","iopub.status.idle":"2025-02-28T06:15:27.727934Z","shell.execute_reply.started":"2025-02-28T06:15:27.711409Z","shell.execute_reply":"2025-02-28T06:15:27.725463Z"}},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"513"},"metadata":{}}],"execution_count":12},{"cell_type":"code","source":"model1_train_data[0:50]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.72971Z","iopub.execute_input":"2025-02-28T06:15:27.730117Z","iopub.status.idle":"2025-02-28T06:15:27.750676Z","shell.execute_reply.started":"2025-02-28T06:15:27.730087Z","shell.execute_reply":"2025-02-28T06:15:27.749222Z"}},"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":" image_id label image_width image_height is_tma\n0 4 2 23785 20008 False\n1 66 3 48871 48195 False\n3 281 3 42309 15545 False\n4 286 1 37204 30020 False\n5 431 2 39991 40943 False\n6 706 2 75606 25965 False\n7 970 2 32131 18935 False\n8 1020 2 36585 33751 False\n9 1080 2 31336 23200 False\n10 1101 2 26306 18403 False\n11 1252 2 60420 27480 False\n12 1289 2 43940 26785 False\n13 1295 2 48320 17700 False\n14 1660 0 83340 20447 False\n15 1666 2 69900 16083 False\n16 1774 2 44231 37571 False\n17 1925 2 44847 32951 False\n18 1943 0 73730 34949 False\n19 1952 0 33685 38053 False\n20 2097 2 31696 21984 False\n21 2227 4 60063 40187 False\n22 2391 3 58075 26192 False\n23 2666 1 53270 44031 False\n24 2706 2 71289 22569 False\n25 2906 1 77265 31059 False\n26 3055 2 73631 37323 False\n27 3084 4 44749 42274 False\n28 3092 3 38308 20600 False\n29 3098 2 68160 26751 False\n30 3191 0 10688 23348 False\n31 3222 1 31881 21623 False\n32 3264 2 63480 28313 False\n33 3511 1 29305 17826 False\n34 3672 3 62463 21527 False\n35 3881 2 28073 14116 False\n36 3997 4 49467 29610 False\n38 4211 2 35243 34431 False\n39 4608 1 33155 39867 False\n40 4797 4 53623 38587 False\n41 4827 0 57240 31509 False\n42 4877 0 57724 42954 False\n43 4963 2 40708 48923 False\n44 5015 2 48645 44254 False\n45 5114 2 33646 29171 False\n46 5251 2 105763 18704 False\n47 5264 2 45054 22207 False\n48 5265 1 63815 44795 False\n49 5307 2 71975 27683 False\n50 5456 4 45663 45226 False\n51 5851 2 59353 28393 False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idlabelimage_widthimage_heightis_tma
0422378520008False
16634887148195False
328134230915545False
428613720430020False
543123999140943False
670627560625965False
797023213118935False
8102023658533751False
9108023133623200False
10110122630618403False
11125226042027480False
12128924394026785False
13129524832017700False
14166008334020447False
15166626990016083False
16177424423137571False
17192524484732951False
18194307373034949False
19195203368538053False
20209723169621984False
21222746006340187False
22239135807526192False
23266615327044031False
24270627128922569False
25290617726531059False
26305527363137323False
27308444474942274False
28309233830820600False
29309826816026751False
30319101068823348False
31322213188121623False
32326426348028313False
33351112930517826False
34367236246321527False
35388122807314116False
36399744946729610False
38421123524334431False
39460813315539867False
40479745362338587False
41482705724031509False
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43496324070848923False
44501524864544254False
45511423364629171False
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49530727197527683False
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"},"metadata":{}}],"execution_count":13},{"cell_type":"code","source":"model1_train_data = model1_train_data[0:50] # Only first 50 elements will be used to train Model 1\nmodel1_test_data = model1_test_data[0:12] # Only first 12 of 25 TMAs available are used to test the model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.752662Z","iopub.execute_input":"2025-02-28T06:15:27.753084Z","iopub.status.idle":"2025-02-28T06:15:27.761967Z","shell.execute_reply.started":"2025-02-28T06:15:27.753054Z","shell.execute_reply":"2025-02-28T06:15:27.760691Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"Image.MAX_IMAGE_PIXELS = 10000000000\n# Define patch size and overlap (if needed)\npatch_size = (128,128) # Adjust this according to your requirements\noverlap = 0 # Adjust this if you want overlapping patches\n\nimage_data = []\nimage_label = []\nempty_img=0\nfor img_id, label , tma in zip(model1_train_data['image_id'],model1_train_data['label'], model1_train_data['is_tma']):\n #print(img_id, label, tma)\n if tma==0:\n img_name = str(img_id)+\"_thumbnail.png\"\n large_image = Image.open(\"/kaggle/input/UBC-OCEAN/train_thumbnails/\"+img_name)\n for y in range(0, large_image.height, patch_size[0] - overlap): # (0,2523,192)\n for x in range(0, large_image.width, patch_size[1] - overlap): # (0,3000,192) 224-32=192\n patch = large_image.crop((x, y, x+patch_size[1], y+patch_size[0]))\n image = np.array(patch)\n if np.sum(image)==0:\n empty_img+=1\n elif (np.sum(image[0:,0:50])==0) or (np.sum(image[0:,50:])==0) or (np.sum(image[0:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:])==0) or (np.sum(image[50:,0:])==0) or (np.sum(image[100:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:50])==0) or (np.sum(image[0:50,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:100,0:50])==0) or (np.sum(image[50:100,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:,0:100])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,75:])==0) or (np.sum(image[50:,75:])==0):\n empty_img+=1\n elif (np.sum(image[0:40,80:])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:35,0:])==0) or (np.sum(image[80:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:50])==0) or (np.sum(image[0:25,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:25])==0) or (np.sum(image[0:20,25:50])==0) or (np.sum(image[0:20,50:80])==0) or (np.sum(image[0:20,90:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:25])==0) or (np.sum(image[0:25,100:])==0) or (np.sum(image[100:,0:25])==0) or (np.sum(image[100:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:15,0:])==0) or (np.sum(image[115:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:,0:15])==0) or (np.sum(image[0:,115:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:20])==0) or (np.sum(image[0:20,110:])==0) or (np.sum(image[110:,0:20])==0) or (np.sum(image[110:,110:])==0):\n empty_img+=1\n elif (np.sum(image[0:10,0:10])==0) or (np.sum(image[0:10,115:])==0) or (np.sum(image[0:10,40:60])==0) or (np.sum(image[0:10,80:100])==0):\n empty_img+=1\n elif (np.sum(image[40:50,0:10])==0) or (np.sum(image[110:,80:100])==0) or (np.sum(image[70:85,70:90])==0) or (np.sum(image[50:60,110:])==0):\n empty_img+=1\n\n else:\n image_data.append(image)\n image_label.append(label)\n \n elif tma==1:\n img_name = str(img_id)+\".png\"\n large_image = Image.open(\"/kaggle/input/UBC-OCEAN/train_images/\"+img_name)\n for y in range(0, large_image.height, patch_size[0] - overlap): # (0,2523,192)\n for x in range(0, large_image.width, patch_size[1] - overlap): # (0,3000,192) 224-32=192\n patch = large_image.crop((x, y, x+patch_size[1], y+patch_size[0]))\n image = np.array(patch)\n if np.sum(image)==0:\n empty_img+=1\n elif (np.sum(image[0:,0:50])==0) or (np.sum(image[0:,50:])==0) or (np.sum(image[0:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:])==0) or (np.sum(image[50:,0:])==0) or (np.sum(image[100:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:50])==0) or (np.sum(image[0:50,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:100,0:50])==0) or (np.sum(image[50:100,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:,0:100])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,75:])==0) or (np.sum(image[50:,75:])==0):\n empty_img+=1\n elif (np.sum(image[0:40,80:])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:35,0:])==0) or (np.sum(image[80:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:50])==0) or (np.sum(image[0:25,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:25])==0) or (np.sum(image[0:20,25:50])==0) or (np.sum(image[0:20,50:80])==0) or (np.sum(image[0:20,90:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:25])==0) or (np.sum(image[0:25,100:])==0) or (np.sum(image[100:,0:25])==0) or (np.sum(image[100:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:15,0:])==0) or (np.sum(image[115:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:,0:15])==0) or (np.sum(image[0:,115:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:20])==0) or (np.sum(image[0:20,110:])==0) or (np.sum(image[110:,0:20])==0) or (np.sum(image[110:,110:])==0):\n empty_img+=1\n elif (np.sum(image[0:10,0:10])==0) or (np.sum(image[0:10,115:])==0) or (np.sum(image[0:10,40:60])==0) or (np.sum(image[0:10,80:100])==0):\n empty_img+=1\n elif (np.sum(image[40:50,0:10])==0) or (np.sum(image[110:,80:100])==0) or (np.sum(image[70:85,70:90])==0) or (np.sum(image[50:60,110:])==0):\n empty_img+=1\n \n \n else:\n image_data.append(image)\n image_label.append(label)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:27.763722Z","iopub.execute_input":"2025-02-28T06:15:27.764131Z","iopub.status.idle":"2025-02-28T06:15:49.908926Z","shell.execute_reply.started":"2025-02-28T06:15:27.764101Z","shell.execute_reply":"2025-02-28T06:15:49.90768Z"}},"outputs":[],"execution_count":15},{"cell_type":"code","source":"model1_train_image_data = image_data\nprint(len(model1_train_image_data))\n\nmodel1_train_image_label = image_label\nprint(len(model1_train_image_label))\n\nprint(empty_img)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:49.910483Z","iopub.execute_input":"2025-02-28T06:15:49.910964Z","iopub.status.idle":"2025-02-28T06:15:49.928806Z","shell.execute_reply.started":"2025-02-28T06:15:49.910916Z","shell.execute_reply":"2025-02-28T06:15:49.927596Z"}},"outputs":[{"name":"stdout","text":"8879\n8879\n10225\n","output_type":"stream"}],"execution_count":16},{"cell_type":"code","source":"model1_train_image_data[0].shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:49.930572Z","iopub.execute_input":"2025-02-28T06:15:49.931129Z","iopub.status.idle":"2025-02-28T06:15:49.94887Z","shell.execute_reply.started":"2025-02-28T06:15:49.931087Z","shell.execute_reply":"2025-02-28T06:15:49.947835Z"}},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"(128, 128, 3)"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"model1_train_image_data[0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:49.952727Z","iopub.execute_input":"2025-02-28T06:15:49.953211Z","iopub.status.idle":"2025-02-28T06:15:49.969601Z","shell.execute_reply.started":"2025-02-28T06:15:49.953171Z","shell.execute_reply":"2025-02-28T06:15:49.968347Z"}},"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"array([[[239, 237, 238],\n [239, 236, 238],\n [239, 237, 238],\n ...,\n [ 1, 1, 1],\n [ 0, 0, 0],\n [ 0, 0, 0]],\n\n [[239, 236, 238],\n [239, 237, 238],\n [239, 237, 239],\n ...,\n [ 3, 3, 3],\n [ 1, 1, 1],\n [ 0, 0, 0]],\n\n [[239, 236, 237],\n [239, 236, 238],\n [239, 236, 238],\n ...,\n [ 0, 0, 0],\n [ 3, 3, 3],\n [ 0, 0, 0]],\n\n ...,\n\n [[190, 139, 169],\n [191, 156, 182],\n [194, 156, 182],\n ...,\n [202, 176, 194],\n [196, 173, 192],\n [202, 177, 195]],\n\n [[178, 126, 159],\n [189, 139, 168],\n [191, 148, 174],\n ...,\n [203, 175, 195],\n [198, 173, 193],\n [205, 187, 202]],\n\n [[182, 137, 167],\n [191, 132, 164],\n [198, 168, 191],\n ...,\n [190, 158, 182],\n [205, 175, 195],\n [220, 205, 216]]], dtype=uint8)"},"metadata":{}}],"execution_count":18},{"cell_type":"code","source":"x_train = np.array(model1_train_image_data) \ny_train = np.array(model1_train_image_label)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:49.971448Z","iopub.execute_input":"2025-02-28T06:15:49.972018Z","iopub.status.idle":"2025-02-28T06:15:50.212715Z","shell.execute_reply.started":"2025-02-28T06:15:49.971986Z","shell.execute_reply":"2025-02-28T06:15:50.208913Z"}},"outputs":[],"execution_count":19},{"cell_type":"code","source":"print(x_train.shape)\nprint(y_train.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:50.218056Z","iopub.execute_input":"2025-02-28T06:15:50.219432Z","iopub.status.idle":"2025-02-28T06:15:50.236674Z","shell.execute_reply.started":"2025-02-28T06:15:50.219309Z","shell.execute_reply":"2025-02-28T06:15:50.233288Z"}},"outputs":[{"name":"stdout","text":"(8879, 128, 128, 3)\n(8879,)\n","output_type":"stream"}],"execution_count":20},{"cell_type":"code","source":"# Save the NumPy array to a file\n# joblib.dump(x3, 'x_24000.joblib')\n\njoblib.dump(x_train, 'x_data_train.joblib')\njoblib.dump(y_train, 'y_data_train.joblib')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:50.239674Z","iopub.execute_input":"2025-02-28T06:15:50.240093Z","iopub.status.idle":"2025-02-28T06:15:52.689334Z","shell.execute_reply.started":"2025-02-28T06:15:50.240052Z","shell.execute_reply":"2025-02-28T06:15:52.687855Z"}},"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"['y_data_train.joblib']"},"metadata":{}}],"execution_count":21},{"cell_type":"code","source":"x_data_train = joblib.load(\"/kaggle/working/x_data_train.joblib\")\ny_data_train = joblib.load(\"/kaggle/working/y_data_train.joblib\")\nprint(x_data_train.shape)\nprint(y_data_train.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:52.691064Z","iopub.execute_input":"2025-02-28T06:15:52.691502Z","iopub.status.idle":"2025-02-28T06:15:52.981306Z","shell.execute_reply.started":"2025-02-28T06:15:52.691462Z","shell.execute_reply":"2025-02-28T06:15:52.979229Z"}},"outputs":[{"name":"stdout","text":"(8879, 128, 128, 3)\n(8879,)\n","output_type":"stream"}],"execution_count":22},{"cell_type":"markdown","source":"Proceed the same preprocessing, but with testing data (corresponding to is_tma=True).","metadata":{}},{"cell_type":"code","source":"model1_test_data = data[data[\"is_tma\"]==True]\nmodel1_test_data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:52.982962Z","iopub.execute_input":"2025-02-28T06:15:52.983366Z","iopub.status.idle":"2025-02-28T06:15:53.017114Z","shell.execute_reply.started":"2025-02-28T06:15:52.983315Z","shell.execute_reply":"2025-02-28T06:15:53.01461Z"}},"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":" image_id label image_width image_height is_tma\n2 91 HGSC 3388 3388 True\n37 4134 MC 2964 2964 True\n76 8280 HGSC 2964 2964 True\n83 9200 MC 3388 3388 True\n112 13568 LGSC 2964 2964 True","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idlabelimage_widthimage_heightis_tma
291HGSC33883388True
374134MC29642964True
768280HGSC29642964True
839200MC33883388True
11213568LGSC29642964True
\n
"},"metadata":{}}],"execution_count":23},{"cell_type":"code","source":"class_labels = ['CC', 'EC', 'HGSC', 'LGSC', 'MC']\nmodel1_test_data['label'] = model1_test_data['label'].replace({'CC':0, 'EC':1, 'HGSC':2, 'LGSC':3, 'MC':4})\nmodel1_test_data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:53.02116Z","iopub.execute_input":"2025-02-28T06:15:53.021537Z","iopub.status.idle":"2025-02-28T06:15:53.057242Z","shell.execute_reply.started":"2025-02-28T06:15:53.021507Z","shell.execute_reply":"2025-02-28T06:15:53.053698Z"}},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":" image_id label image_width image_height is_tma\n2 91 2 3388 3388 True\n37 4134 4 2964 2964 True\n76 8280 2 2964 2964 True\n83 9200 4 3388 3388 True\n112 13568 3 2964 2964 True","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idlabelimage_widthimage_heightis_tma
291233883388True
374134429642964True
768280229642964True
839200433883388True
11213568329642964True
\n
"},"metadata":{}}],"execution_count":24},{"cell_type":"code","source":"Image.MAX_IMAGE_PIXELS = 10000000000\n# Define patch size and overlap (if needed)\npatch_size = (128,128) # Adjust this according to your requirements\noverlap = 0 # Adjust this if you want overlapping patches\n\nimage_data = []\nimage_label = []\nempty_img=0\nfor img_id, label , tma in zip(model1_test_data['image_id'],model1_test_data['label'], model1_test_data['is_tma']):\n #print(img_id, label, tma)\n if tma==0:\n img_name = str(img_id)+\"_thumbnail.png\"\n large_image = Image.open(\"/kaggle/input/UBC-OCEAN/train_thumbnails/\"+img_name)\n for y in range(0, large_image.height, patch_size[0] - overlap): # (0,2523,192)\n for x in range(0, large_image.width, patch_size[1] - overlap): # (0,3000,192) 224-32=192\n patch = large_image.crop((x, y, x+patch_size[1], y+patch_size[0]))\n image = np.array(patch)\n if np.sum(image)==0:\n empty_img+=1\n elif (np.sum(image[0:,0:50])==0) or (np.sum(image[0:,50:])==0) or (np.sum(image[0:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:])==0) or (np.sum(image[50:,0:])==0) or (np.sum(image[100:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:50])==0) or (np.sum(image[0:50,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:100,0:50])==0) or (np.sum(image[50:100,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:,0:100])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,75:])==0) or (np.sum(image[50:,75:])==0):\n empty_img+=1\n elif (np.sum(image[0:40,80:])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:35,0:])==0) or (np.sum(image[80:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:50])==0) or (np.sum(image[0:25,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:25])==0) or (np.sum(image[0:20,25:50])==0) or (np.sum(image[0:20,50:80])==0) or (np.sum(image[0:20,90:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:25])==0) or (np.sum(image[0:25,100:])==0) or (np.sum(image[100:,0:25])==0) or (np.sum(image[100:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:15,0:])==0) or (np.sum(image[115:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:,0:15])==0) or (np.sum(image[0:,115:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:20])==0) or (np.sum(image[0:20,110:])==0) or (np.sum(image[110:,0:20])==0) or (np.sum(image[110:,110:])==0):\n empty_img+=1\n elif (np.sum(image[0:10,0:10])==0) or (np.sum(image[0:10,115:])==0) or (np.sum(image[0:10,40:60])==0) or (np.sum(image[0:10,80:100])==0):\n empty_img+=1\n elif (np.sum(image[40:50,0:10])==0) or (np.sum(image[110:,80:100])==0) or (np.sum(image[70:85,70:90])==0) or (np.sum(image[50:60,110:])==0):\n empty_img+=1\n\n else:\n image_data.append(image)\n image_label.append(label)\n \n elif tma==1:\n img_name = str(img_id)+\".png\"\n large_image = Image.open(\"/kaggle/input/UBC-OCEAN/train_images/\"+img_name)\n for y in range(0, large_image.height, patch_size[0] - overlap): # (0,2523,192)\n for x in range(0, large_image.width, patch_size[1] - overlap): # (0,3000,192) 224-32=192\n patch = large_image.crop((x, y, x+patch_size[1], y+patch_size[0]))\n image = np.array(patch)\n if np.sum(image)==0:\n empty_img+=1\n elif (np.sum(image[0:,0:50])==0) or (np.sum(image[0:,50:])==0) or (np.sum(image[0:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:])==0) or (np.sum(image[50:,0:])==0) or (np.sum(image[100:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,0:50])==0) or (np.sum(image[0:50,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:100,0:50])==0) or (np.sum(image[50:100,50:])==0):\n empty_img+=1\n elif (np.sum(image[50:,0:100])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:50,75:])==0) or (np.sum(image[50:,75:])==0):\n empty_img+=1\n elif (np.sum(image[0:40,80:])==0) or (np.sum(image[80:,80:])==0):\n empty_img+=1\n elif (np.sum(image[0:35,0:])==0) or (np.sum(image[80:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:50])==0) or (np.sum(image[0:25,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:25])==0) or (np.sum(image[0:20,25:50])==0) or (np.sum(image[0:20,50:80])==0) or (np.sum(image[0:20,90:])==0):\n empty_img+=1\n elif (np.sum(image[0:25,0:25])==0) or (np.sum(image[0:25,100:])==0) or (np.sum(image[100:,0:25])==0) or (np.sum(image[100:,100:])==0):\n empty_img+=1\n elif (np.sum(image[0:15,0:])==0) or (np.sum(image[115:,0:])==0):\n empty_img+=1\n elif (np.sum(image[0:,0:15])==0) or (np.sum(image[0:,115:])==0):\n empty_img+=1\n elif (np.sum(image[0:20,0:20])==0) or (np.sum(image[0:20,110:])==0) or (np.sum(image[110:,0:20])==0) or (np.sum(image[110:,110:])==0):\n empty_img+=1\n elif (np.sum(image[0:10,0:10])==0) or (np.sum(image[0:10,115:])==0) or (np.sum(image[0:10,40:60])==0) or (np.sum(image[0:10,80:100])==0):\n empty_img+=1\n elif (np.sum(image[40:50,0:10])==0) or (np.sum(image[110:,80:100])==0) or (np.sum(image[70:85,70:90])==0) or (np.sum(image[50:60,110:])==0):\n empty_img+=1\n \n \n else:\n image_data.append(image)\n image_label.append(label)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:15:53.05963Z","iopub.execute_input":"2025-02-28T06:15:53.060343Z","iopub.status.idle":"2025-02-28T06:16:24.743912Z","shell.execute_reply.started":"2025-02-28T06:15:53.060292Z","shell.execute_reply":"2025-02-28T06:16:24.742674Z"}},"outputs":[],"execution_count":25},{"cell_type":"code","source":"model1_test_image_data = image_data\nprint(len(model1_test_image_data))\n\nmodel1_test_image_label = image_label\nprint(len(model1_test_image_label))\n\nprint(empty_img)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:16:24.745539Z","iopub.execute_input":"2025-02-28T06:16:24.745977Z","iopub.status.idle":"2025-02-28T06:16:24.769494Z","shell.execute_reply.started":"2025-02-28T06:16:24.745931Z","shell.execute_reply":"2025-02-28T06:16:24.768127Z"}},"outputs":[{"name":"stdout","text":"15283\n15283\n1259\n","output_type":"stream"}],"execution_count":26},{"cell_type":"code","source":"model1_test_image_data[0].shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:16:24.771133Z","iopub.execute_input":"2025-02-28T06:16:24.77156Z","iopub.status.idle":"2025-02-28T06:16:24.791994Z","shell.execute_reply.started":"2025-02-28T06:16:24.77152Z","shell.execute_reply":"2025-02-28T06:16:24.790827Z"}},"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"(128, 128, 3)"},"metadata":{}}],"execution_count":27},{"cell_type":"code","source":"model1_test_image_data[0]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:16:24.793322Z","iopub.execute_input":"2025-02-28T06:16:24.793657Z","iopub.status.idle":"2025-02-28T06:16:24.810177Z","shell.execute_reply.started":"2025-02-28T06:16:24.793629Z","shell.execute_reply":"2025-02-28T06:16:24.808852Z"}},"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"array([[[209, 203, 221],\n [207, 202, 222],\n [203, 206, 221],\n ...,\n [198, 184, 208],\n [202, 189, 214],\n [197, 183, 207]],\n\n [[209, 202, 218],\n [210, 204, 222],\n [204, 199, 223],\n ...,\n [197, 185, 207],\n [202, 189, 212],\n [196, 183, 206]],\n\n [[208, 200, 216],\n [213, 206, 221],\n [210, 203, 219],\n ...,\n [196, 186, 206],\n [200, 189, 210],\n [194, 184, 205]],\n\n ...,\n\n [[208, 194, 215],\n [202, 200, 217],\n [211, 204, 224],\n ...,\n [199, 193, 211],\n [205, 194, 215],\n [206, 192, 213]],\n\n [[199, 193, 211],\n [199, 192, 212],\n [203, 195, 214],\n ...,\n [199, 190, 211],\n [198, 189, 210],\n [201, 190, 212]],\n\n [[193, 196, 213],\n [202, 190, 218],\n [208, 199, 215],\n ...,\n [198, 188, 211],\n [195, 190, 212],\n [195, 194, 214]]], dtype=uint8)"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"# converting variables on numoy arrays.\nx_test = np.array(model1_test_image_data) \ny_test = np.array(model1_test_image_label)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:16:24.816245Z","iopub.execute_input":"2025-02-28T06:16:24.816598Z","iopub.status.idle":"2025-02-28T06:16:25.613897Z","shell.execute_reply.started":"2025-02-28T06:16:24.816572Z","shell.execute_reply":"2025-02-28T06:16:25.612812Z"}},"outputs":[],"execution_count":29},{"cell_type":"code","source":"# checking variables shape\nprint(x_test.shape)\nprint(y_test.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:16:25.61566Z","iopub.execute_input":"2025-02-28T06:16:25.616156Z","iopub.status.idle":"2025-02-28T06:16:25.622089Z","shell.execute_reply.started":"2025-02-28T06:16:25.616111Z","shell.execute_reply":"2025-02-28T06:16:25.620839Z"}},"outputs":[{"name":"stdout","text":"(15283, 128, 128, 3)\n(15283,)\n","output_type":"stream"}],"execution_count":30},{"cell_type":"code","source":"# exporting variables as job.lib\njoblib.dump(x_test, 'x_data_test.joblib')\njoblib.dump(y_test, 'y_data_test.joblib')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:16:25.623465Z","iopub.execute_input":"2025-02-28T06:16:25.623882Z","iopub.status.idle":"2025-02-28T06:16:28.922622Z","shell.execute_reply.started":"2025-02-28T06:16:25.623852Z","shell.execute_reply":"2025-02-28T06:16:28.921418Z"}},"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"['y_data_test.joblib']"},"metadata":{}}],"execution_count":31},{"cell_type":"code","source":"# loading .joblib file on working folder\nx_data_test = joblib.load(\"/kaggle/working/x_data_test.joblib\")\ny_data_test = joblib.load(\"/kaggle/working/y_data_test.joblib\")\nprint(x_data_test.shape)\nprint(y_data_test.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:16:28.924045Z","iopub.execute_input":"2025-02-28T06:16:28.924484Z","iopub.status.idle":"2025-02-28T06:16:29.580942Z","shell.execute_reply.started":"2025-02-28T06:16:28.924456Z","shell.execute_reply":"2025-02-28T06:16:29.579722Z"}},"outputs":[{"name":"stdout","text":"(15283, 128, 128, 3)\n(15283,)\n","output_type":"stream"}],"execution_count":32},{"cell_type":"code","source":"# matching independent and target variables of training and testing datasets for model 1\nx_train = x_data_train \nx_test = x_data_test\ny_train = y_data_train\ny_test = y_data_test","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:16:29.582362Z","iopub.execute_input":"2025-02-28T06:16:29.582979Z","iopub.status.idle":"2025-02-28T06:16:30.459815Z","shell.execute_reply.started":"2025-02-28T06:16:29.582944Z","shell.execute_reply":"2025-02-28T06:16:30.458562Z"}},"outputs":[],"execution_count":33},{"cell_type":"code","source":"# Scaling the data for train images\nx_train_scaled = x_train/255\nx_test_scaled = x_test/255","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:16:30.461433Z","iopub.execute_input":"2025-02-28T06:16:30.461814Z","iopub.status.idle":"2025-02-28T06:16:37.097049Z","shell.execute_reply.started":"2025-02-28T06:16:30.46175Z","shell.execute_reply":"2025-02-28T06:16:37.095672Z"}},"outputs":[],"execution_count":34},{"cell_type":"code","source":"# Model building using VGG16 Model available on Keras\n\nfrom tensorflow.keras.applications import VGG16\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Dense, Flatten\nfrom tensorflow.keras.optimizers import Adam\nnum_classes = 5\n# Load the VGG16 model with ImageNet weights and exclude the top classification layer\nbase_model = VGG16(weights='imagenet', include_top=False, input_shape=(128,128, 3))\n\n# Customize the top classification layers\nx = base_model.output\nx = Flatten()(x)\nx = Dense(1000, activation='relu')(x) #original is 4096\nx = Dense(1000, activation='relu')(x) #original is 4096\npredictions = Dense(num_classes, activation='softmax')(x) # Replace num_classes with the number of classes in your dataset\n\n# Create the VGG16 model with your custom top layer\nmodel = Model(inputs=base_model.input, outputs=predictions)\n\n# Freeze pre-trained layers (optional)\nfor layer in base_model.layers:\n layer.trainable = False\n\nmodel.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:16:37.098578Z","iopub.execute_input":"2025-02-28T06:16:37.099Z","iopub.status.idle":"2025-02-28T06:16:37.805157Z","shell.execute_reply.started":"2025-02-28T06:16:37.098962Z","shell.execute_reply":"2025-02-28T06:16:37.803604Z"}},"outputs":[{"name":"stdout","text":"Model: \"model_1\"\n_________________________________________________________________\n Layer (type) Output Shape Param # \n=================================================================\n input_2 (InputLayer) [(None, 128, 128, 3)] 0 \n \n block1_conv1 (Conv2D) (None, 128, 128, 64) 1792 \n \n block1_conv2 (Conv2D) (None, 128, 128, 64) 36928 \n \n block1_pool (MaxPooling2D) (None, 64, 64, 64) 0 \n \n block2_conv1 (Conv2D) (None, 64, 64, 128) 73856 \n \n block2_conv2 (Conv2D) (None, 64, 64, 128) 147584 \n \n block2_pool (MaxPooling2D) (None, 32, 32, 128) 0 \n \n block3_conv1 (Conv2D) (None, 32, 32, 256) 295168 \n \n block3_conv2 (Conv2D) (None, 32, 32, 256) 590080 \n \n block3_conv3 (Conv2D) (None, 32, 32, 256) 590080 \n \n block3_pool (MaxPooling2D) (None, 16, 16, 256) 0 \n \n block4_conv1 (Conv2D) (None, 16, 16, 512) 1180160 \n \n block4_conv2 (Conv2D) (None, 16, 16, 512) 2359808 \n \n block4_conv3 (Conv2D) (None, 16, 16, 512) 2359808 \n \n block4_pool (MaxPooling2D) (None, 8, 8, 512) 0 \n \n block5_conv1 (Conv2D) (None, 8, 8, 512) 2359808 \n \n block5_conv2 (Conv2D) (None, 8, 8, 512) 2359808 \n \n block5_conv3 (Conv2D) (None, 8, 8, 512) 2359808 \n \n block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 \n \n flatten_2 (Flatten) (None, 8192) 0 \n \n dense_5 (Dense) (None, 1000) 8193000 \n \n dense_6 (Dense) (None, 1000) 1001000 \n \n dense_7 (Dense) (None, 5) 5005 \n \n=================================================================\nTotal params: 23,913,693\nTrainable params: 9,199,005\nNon-trainable params: 14,714,688\n_________________________________________________________________\n","output_type":"stream"}],"execution_count":35},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Conv2D, Flatten, MaxPooling2D\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.datasets import mnist\nfrom tensorflow.keras.utils import to_categorical\n\n# Load and preprocess the dataset (e.g., MNIST dataset)\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\n# Reshape the data to include channel dimension and normalize\nx_train_scaled = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.0\nx_test_scaled = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.0\n\n# Initialize a simple model (e.g., CNN for image classification)\nmodel = Sequential([\n Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n MaxPooling2D(pool_size=(2, 2)),\n Conv2D(64, (3, 3), activation='relu'),\n MaxPooling2D(pool_size=(2, 2)),\n Flatten(),\n Dense(128, activation='relu'),\n Dense(10, activation='softmax')\n])\n\n# Compile the model with optimizer, loss, and metric\nmodel.compile(optimizer=Adam(lr=0.0001),\n loss='sparse_categorical_crossentropy',\n metrics=['accuracy'])\n\n# Train the model\nhistory = model.fit(x_train_scaled, y_train, \n epochs=15, \n batch_size=64, \n validation_data=(x_test_scaled, y_test))\n\n# Evaluate the model on the test set\ntest_loss, test_accuracy = model.evaluate(x_test_scaled, y_test)\nprint(f'Test accuracy: {test_accuracy:.4f}')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:16:37.807164Z","iopub.execute_input":"2025-02-28T06:16:37.807682Z","iopub.status.idle":"2025-02-28T06:24:45.772433Z","shell.execute_reply.started":"2025-02-28T06:16:37.807637Z","shell.execute_reply":"2025-02-28T06:24:45.771191Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/15\n938/938 [==============================] - 32s 33ms/step - loss: 0.1695 - accuracy: 0.9491 - val_loss: 0.0562 - val_accuracy: 0.9822\nEpoch 2/15\n938/938 [==============================] - 32s 34ms/step - loss: 0.0529 - accuracy: 0.9835 - val_loss: 0.0348 - val_accuracy: 0.9880\nEpoch 3/15\n938/938 [==============================] - 31s 33ms/step - loss: 0.0362 - accuracy: 0.9886 - val_loss: 0.0398 - val_accuracy: 0.9868\nEpoch 4/15\n938/938 [==============================] - 31s 33ms/step - loss: 0.0263 - accuracy: 0.9916 - val_loss: 0.0291 - val_accuracy: 0.9896\nEpoch 5/15\n938/938 [==============================] - 32s 34ms/step - loss: 0.0202 - accuracy: 0.9936 - val_loss: 0.0288 - val_accuracy: 0.9900\nEpoch 6/15\n938/938 [==============================] - 32s 34ms/step - loss: 0.0154 - accuracy: 0.9952 - val_loss: 0.0333 - val_accuracy: 0.9894\nEpoch 7/15\n938/938 [==============================] - 33s 35ms/step - loss: 0.0131 - accuracy: 0.9955 - val_loss: 0.0292 - val_accuracy: 0.9907\nEpoch 8/15\n938/938 [==============================] - 32s 34ms/step - loss: 0.0057 - accuracy: 0.9982 - val_loss: 0.0402 - val_accuracy: 0.9898\nEpoch 12/15\n938/938 [==============================] - 32s 34ms/step - loss: 0.0056 - accuracy: 0.9980 - val_loss: 0.0520 - val_accuracy: 0.9877\nEpoch 13/15\n938/938 [==============================] - 32s 35ms/step - loss: 0.0056 - accuracy: 0.9980 - val_loss: 0.0304 - val_accuracy: 0.9916\nEpoch 14/15\n938/938 [==============================] - 31s 33ms/step - loss: 0.0049 - accuracy: 0.9984 - val_loss: 0.0364 - val_accuracy: 0.9917\nEpoch 15/15\n938/938 [==============================] - 32s 34ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.0371 - val_accuracy: 0.9910\n313/313 [==============================] - 2s 7ms/step - loss: 0.0371 - accuracy: 0.9910\nTest accuracy: 0.9910\n","output_type":"stream"}],"execution_count":36},{"cell_type":"code","source":"# Evaluate the model on training data\ntrain_loss, train_acc = model.evaluate(x_train_scaled, y_train, verbose=0)\nprint(f\"Accuracy on Train Data: {train_acc * 100:.2f}%\")\n\n# Evaluate the model on test data\ntest_loss, test_acc = model.evaluate(x_test_scaled, y_test, verbose=0)\nprint(f\"Accuracy on Test Data: {test_acc * 100:.2f}%\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:24:45.774203Z","iopub.execute_input":"2025-02-28T06:24:45.774683Z","iopub.status.idle":"2025-02-28T06:25:01.072099Z","shell.execute_reply.started":"2025-02-28T06:24:45.774641Z","shell.execute_reply":"2025-02-28T06:25:01.070652Z"}},"outputs":[{"name":"stdout","text":"Accuracy on Train Data: 99.91%\nAccuracy on Test Data: 99.10%\n","output_type":"stream"}],"execution_count":37},{"cell_type":"code","source":"y_pred = model.predict(x_test_scaled)\ny_pred_test = [np.argmax(i) for i in y_pred]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:25:01.073787Z","iopub.execute_input":"2025-02-28T06:25:01.074193Z","iopub.status.idle":"2025-02-28T06:25:04.288174Z","shell.execute_reply.started":"2025-02-28T06:25:01.07416Z","shell.execute_reply":"2025-02-28T06:25:04.286992Z"}},"outputs":[{"name":"stdout","text":"313/313 [==============================] - 3s 9ms/step\n","output_type":"stream"}],"execution_count":38},{"cell_type":"code","source":"len(y_pred_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:25:04.289554Z","iopub.execute_input":"2025-02-28T06:25:04.289913Z","iopub.status.idle":"2025-02-28T06:25:04.297978Z","shell.execute_reply.started":"2025-02-28T06:25:04.289883Z","shell.execute_reply":"2025-02-28T06:25:04.296703Z"}},"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"10000"},"metadata":{}}],"execution_count":39},{"cell_type":"code","source":"# Save the model to a file\nmodel.save(\"UBC-OCEAN-CHL1-model1.h5\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:25:04.299352Z","iopub.execute_input":"2025-02-28T06:25:04.299642Z","iopub.status.idle":"2025-02-28T06:25:04.357991Z","shell.execute_reply.started":"2025-02-28T06:25:04.299618Z","shell.execute_reply":"2025-02-28T06:25:04.356254Z"}},"outputs":[],"execution_count":40},{"cell_type":"code","source":"# Metrics evaluation on test data\nprint(\"Confusion Matrix:\\n\",confusion_matrix(y_test,y_pred_test))\nprint()\nprint(\"Classification Report:\\n\",classification_report(y_test,y_pred_test))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-28T06:25:04.359875Z","iopub.execute_input":"2025-02-28T06:25:04.360335Z","iopub.status.idle":"2025-02-28T06:25:04.418438Z","shell.execute_reply.started":"2025-02-28T06:25:04.360286Z","shell.execute_reply":"2025-02-28T06:25:04.417104Z"}},"outputs":[{"name":"stdout","text":"Confusion Matrix:\n [[ 976 0 2 0 0 0 0 1 0 1]\n [ 0 1133 0 1 0 1 0 0 0 0]\n [ 1 1 1025 0 0 0 0 5 0 0]\n [ 0 0 2 1002 0 3 0 0 3 0]\n [ 0 0 0 0 977 0 1 0 0 4]\n [ 0 0 0 5 0 887 0 0 0 0]\n [ 1 3 1 0 3 6 944 0 0 0]\n [ 0 6 4 0 0 0 0 1018 0 0]\n [ 3 1 5 1 0 3 0 1 958 2]\n [ 1 6 0 0 5 5 0 2 0 990]]\n\nClassification Report:\n precision recall f1-score support\n\n 0 0.99 1.00 0.99 980\n 1 0.99 1.00 0.99 1135\n 2 0.99 0.99 0.99 1032\n 3 0.99 0.99 0.99 1010\n 4 0.99 0.99 0.99 982\n 5 0.98 0.99 0.99 892\n 6 1.00 0.99 0.99 958\n 7 0.99 0.99 0.99 1028\n 8 1.00 0.98 0.99 974\n 9 0.99 0.98 0.99 1009\n\n accuracy 0.99 10000\n macro avg 0.99 0.99 0.99 10000\nweighted avg 0.99 0.99 0.99 10000\n\n","output_type":"stream"}],"execution_count":41}]}