def train(): x = np.load('data/x_train.npy') y = np.load('data/y_train.npy') y = tf.keras.utils.to_categorical(y, 4) model = keras_model.keras_model_build() # model.summary() opt = tf.keras.optimizers.Adam(lr=0.00001) model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) hist = model.fit(x, y, batch_size=32, epochs=100, verbose=1) model.save('model.h5') y_pred = model.predict(x) confusion_matrix_info(np.argmax(y, axis=1), np.argmax(y_pred, axis=1), title='confusion_matrix_train')
# Define traing params #******************************************* learning_rate = 0.00002 batch_size = 6 display_step = 1 epochs = 100 class_weights = [1, 1, 12] generator = BalanceCovidDataset(data_dir=data_path, csv_file=train_csv, covid_percent=0.3, class_weights=class_weights, batch_size=batch_size) total_batch = len(generator) model = keras_model_build() opt = tf.keras.optimizers.Adam(lr=learning_rate) # ******************************************** # Train model #********************************************* model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) hist = model.fit(generator, steps_per_epoch=total_batch, epochs=epochs, verbose=1, validation_data=(x_test, y_test_c),