{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"##### Transfer learning model - ResNet50 con otros parámetros\n\nlearning Rate con 1e-4","metadata":{}},{"cell_type":"code","source":"pip install plot-keras-history","metadata":{"execution":{"iopub.status.busy":"2023-07-01T06:55:27.961115Z","iopub.execute_input":"2023-07-01T06:55:27.961456Z","iopub.status.idle":"2023-07-01T06:55:44.716045Z","shell.execute_reply.started":"2023-07-01T06:55:27.961426Z","shell.execute_reply":"2023-07-01T06:55:44.714909Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow.keras as keras\nfrom collections import defaultdict\nimport warnings\nfrom plot_keras_history import show_history, plot_history\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2023-07-01T06:55:44.71849Z","iopub.execute_input":"2023-07-01T06:55:44.718868Z","iopub.status.idle":"2023-07-01T06:55:49.9963Z","shell.execute_reply.started":"2023-07-01T06:55:44.718827Z","shell.execute_reply":"2023-07-01T06:55:49.995376Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"training = keras.preprocessing.image_dataset_from_directory(directory = '/kaggle/input/data-binary/data/train')","metadata":{"execution":{"iopub.status.busy":"2023-07-01T06:55:49.998004Z","iopub.execute_input":"2023-07-01T06:55:49.998369Z","iopub.status.idle":"2023-07-01T06:58:10.728447Z","shell.execute_reply.started":"2023-07-01T06:55:49.998331Z","shell.execute_reply":"2023-07-01T06:58:10.727525Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"validation = keras.preprocessing.image_dataset_from_directory(directory = '/kaggle/input/data-binary/data/valid')","metadata":{"execution":{"iopub.status.busy":"2023-07-01T06:58:10.729822Z","iopub.execute_input":"2023-07-01T06:58:10.730221Z","iopub.status.idle":"2023-07-01T07:04:22.595807Z","shell.execute_reply.started":"2023-07-01T06:58:10.730178Z","shell.execute_reply":"2023-07-01T07:04:22.594982Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = keras.preprocessing.image_dataset_from_directory(directory = '/kaggle/input/data-binary/data/test', shuffle=False)","metadata":{"execution":{"iopub.status.busy":"2023-07-01T07:04:22.59925Z","iopub.execute_input":"2023-07-01T07:04:22.599641Z","iopub.status.idle":"2023-07-01T07:12:12.512787Z","shell.execute_reply.started":"2023-07-01T07:04:22.599602Z","shell.execute_reply":"2023-07-01T07:12:12.511809Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Instanciamos el modelo","metadata":{}},{"cell_type":"code","source":"models = defaultdict(None)","metadata":{"execution":{"iopub.status.busy":"2023-07-01T07:12:12.515616Z","iopub.execute_input":"2023-07-01T07:12:12.516264Z","iopub.status.idle":"2023-07-01T07:12:12.520607Z","shell.execute_reply.started":"2023-07-01T07:12:12.516218Z","shell.execute_reply":"2023-07-01T07:12:12.519551Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\n\nweights = \"imagenet\"  # TPesos que de la red\ninclude_top = False  # Si queremos incluir las capas al principio de la red\ninput_shape=(256, 256, 3)\narguments = {\"weights\": weights, \"include_top\": include_top, \"input_shape\": input_shape}\nmodels[\"Transfer learning\"] = tf.keras.applications.ResNet50(**arguments)\nwarnings.filterwarnings(\"ignore\")","metadata":{"execution":{"iopub.status.busy":"2023-07-01T07:12:12.522707Z","iopub.execute_input":"2023-07-01T07:12:12.52314Z","iopub.status.idle":"2023-07-01T07:12:15.040849Z","shell.execute_reply.started":"2023-07-01T07:12:12.523102Z","shell.execute_reply":"2023-07-01T07:12:15.039879Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Congelar las capas del modelo base para evitar que se actualicen durante el entrenamiento","metadata":{}},{"cell_type":"code","source":"models[\"Transfer learning\"].trainable = False","metadata":{"execution":{"iopub.status.busy":"2023-07-01T07:12:15.042197Z","iopub.execute_input":"2023-07-01T07:12:15.042549Z","iopub.status.idle":"2023-07-01T07:12:15.054484Z","shell.execute_reply.started":"2023-07-01T07:12:15.042511Z","shell.execute_reply":"2023-07-01T07:12:15.053615Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Extraemos el vector de características profundas aplanando la salida.","metadata":{}},{"cell_type":"code","source":"x = models[\"Transfer learning\"].output\narguments = {\"data_format\": \"channels_last\"}\nx = keras.layers.Flatten(**arguments)(x)","metadata":{"execution":{"iopub.status.busy":"2023-07-01T07:12:15.056065Z","iopub.execute_input":"2023-07-01T07:12:15.056504Z","iopub.status.idle":"2023-07-01T07:12:15.067905Z","shell.execute_reply.started":"2023-07-01T07:12:15.056465Z","shell.execute_reply":"2023-07-01T07:12:15.067123Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Añadimos la capa completamente conectada y la capa de salida.","metadata":{}},{"cell_type":"markdown","source":"Cuando utilizas la activación sigmoid y estableces units en 1, la capa de salida generará una única unidad de salida con un valor en el rango [0, 1]. Este valor representa la probabilidad de que la muestra pertenezca a la clase positiva (en tu caso, \"Embolia\"). Un valor cercano a 0 indica una baja probabilidad de pertenencia a la clase positiva, mientras que un valor cercano a 1 indica una alta probabilidad.\n\nEn cambio, cuando utilizas la activación softmax y estableces units en 2, la capa de salida generará dos unidades de salida, una para cada clase (\"Embolia\" y \"No embolia\"). La activación softmax aplica una función exponencial a los valores de salida, normalizándolos para que sumen 1 y generen una distribución de probabilidad. Cada valor de salida representará la probabilidad de que la muestra pertenezca a la clase correspondiente. La clase con la probabilidad más alta será la clase asignada a la muestra.","metadata":{}},{"cell_type":"code","source":"units = 128\nactivation = \"relu\"\narguments = {\"units\": units, \"activation\": activation} \nx = keras.layers.Dense(**arguments)(x)\n\nunits = 1\nactivation = \"sigmoid\"\narguments = {\"units\": units, \"activation\": activation} \noutputs = keras.layers.Dense(**arguments)(x)","metadata":{"execution":{"iopub.status.busy":"2023-07-01T07:12:15.069563Z","iopub.execute_input":"2023-07-01T07:12:15.069961Z","iopub.status.idle":"2023-07-01T07:12:15.09433Z","shell.execute_reply.started":"2023-07-01T07:12:15.069924Z","shell.execute_reply":"2023-07-01T07:12:15.093631Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Agurpamos todo","metadata":{}},{"cell_type":"code","source":"inputs = models[\"Transfer learning\"].input\narguments = {\"inputs\": inputs, \"outputs\": outputs}\nmodels[\"Transfer learning\"] = keras.Model(**arguments)\nmodels[\"Transfer learning\"].summary()","metadata":{"execution":{"iopub.status.busy":"2023-07-01T07:12:15.097333Z","iopub.execute_input":"2023-07-01T07:12:15.097598Z","iopub.status.idle":"2023-07-01T07:12:15.181748Z","shell.execute_reply.started":"2023-07-01T07:12:15.097571Z","shell.execute_reply":"2023-07-01T07:12:15.181032Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Compilamos","metadata":{}},{"cell_type":"code","source":"histories = defaultdict(None)  # Dictionary with the histories","metadata":{"execution":{"iopub.status.busy":"2023-07-01T07:12:15.185969Z","iopub.execute_input":"2023-07-01T07:12:15.186293Z","iopub.status.idle":"2023-07-01T07:12:15.192551Z","shell.execute_reply.started":"2023-07-01T07:12:15.186265Z","shell.execute_reply":"2023-07-01T07:12:15.191816Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"loss = keras.losses.BinaryCrossentropy()\n\nmodels[\"Transfer learning\"].compile(loss=loss,\n              optimizer=keras.optimizers.Adam(1e-4),\n              metrics=[keras.metrics.AUC(),keras.metrics.Precision(),\n                       keras.metrics.Recall(), keras.metrics.BinaryAccuracy()])","metadata":{"execution":{"iopub.status.busy":"2023-07-01T07:12:15.19458Z","iopub.execute_input":"2023-07-01T07:12:15.194844Z","iopub.status.idle":"2023-07-01T07:12:15.249515Z","shell.execute_reply.started":"2023-07-01T07:12:15.194819Z","shell.execute_reply":"2023-07-01T07:12:15.248804Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"histories['Transfer learning'] = models['Transfer learning'].fit(training, epochs=7, validation_data=validation, verbose=1)","metadata":{"execution":{"iopub.status.busy":"2023-07-01T07:17:53.995538Z","iopub.execute_input":"2023-07-01T07:17:53.995913Z","iopub.status.idle":"2023-07-01T11:34:50.038831Z","shell.execute_reply.started":"2023-07-01T07:17:53.995862Z","shell.execute_reply":"2023-07-01T11:34:50.037979Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" models['Transfer learning'].save('/kaggle/working/model-ResNet50-TransferLearning-1e-4.h5')","metadata":{"execution":{"iopub.status.busy":"2023-07-01T11:34:50.043139Z","iopub.execute_input":"2023-07-01T11:34:50.043453Z","iopub.status.idle":"2023-07-01T11:34:50.785215Z","shell.execute_reply.started":"2023-07-01T11:34:50.043418Z","shell.execute_reply":"2023-07-01T11:34:50.784366Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for model in histories:\n    # Plot the model training history\n    history = histories[model]\n    plot_history(history)","metadata":{"execution":{"iopub.status.busy":"2023-07-01T11:34:50.787974Z","iopub.execute_input":"2023-07-01T11:34:50.788635Z","iopub.status.idle":"2023-07-01T11:34:52.32864Z","shell.execute_reply.started":"2023-07-01T11:34:50.788577Z","shell.execute_reply":"2023-07-01T11:34:52.327588Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Evaluamos el modelo","metadata":{}},{"cell_type":"code","source":"results = models[\"Transfer learning\"].evaluate(test, batch_size=128)","metadata":{"execution":{"iopub.status.busy":"2023-07-01T11:54:58.32293Z","iopub.execute_input":"2023-07-01T11:54:58.323336Z","iopub.status.idle":"2023-07-01T13:00:49.176697Z","shell.execute_reply.started":"2023-07-01T11:54:58.323303Z","shell.execute_reply":"2023-07-01T13:00:49.175937Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"test loss, test auc, test precision, test recall:\", results)","metadata":{"execution":{"iopub.status.busy":"2023-07-01T13:00:49.179432Z","iopub.execute_input":"2023-07-01T13:00:49.179839Z","iopub.status.idle":"2023-07-01T13:00:49.185777Z","shell.execute_reply.started":"2023-07-01T13:00:49.179796Z","shell.execute_reply":"2023-07-01T13:00:49.184835Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred = models[\"Transfer learning\"].predict(test)","metadata":{"execution":{"iopub.status.busy":"2023-07-01T13:00:49.187146Z","iopub.execute_input":"2023-07-01T13:00:49.187719Z","iopub.status.idle":"2023-07-01T13:27:07.592026Z","shell.execute_reply.started":"2023-07-01T13:00:49.18768Z","shell.execute_reply":"2023-07-01T13:27:07.591161Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\n\ny_pred_binary = np.squeeze(np.round(y_pred)).astype(int)\ny_pred_binary","metadata":{"execution":{"iopub.status.busy":"2023-07-01T13:27:07.595125Z","iopub.execute_input":"2023-07-01T13:27:07.595753Z","iopub.status.idle":"2023-07-01T13:27:07.604099Z","shell.execute_reply.started":"2023-07-01T13:27:07.59571Z","shell.execute_reply":"2023-07-01T13:27:07.602912Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}