inception_flowers_tune.py 文件源码

def train_top_model():
    # Load the bottleneck features and labels
    train_features = np.load(open(output_dir+'bottleneck_features_train.npy', 'rb'))
    train_labels = np.load(open(output_dir+'bottleneck_labels_train.npy', 'rb'))
    validation_features = np.load(open(output_dir+'bottleneck_features_validation.npy', 'rb'))
    validation_labels = np.load(open(output_dir+'bottleneck_labels_validation.npy', 'rb'))

    # Create the top model for the inception V3 network, a single Dense layer
    # with softmax activation.
    top_input = Input(shape=train_features.shape[1:])
    top_output = Dense(5, activation='softmax')(top_input)
    model = Model(top_input, top_output)

    # Train the model using the bottleneck features and save the weights.
    model.compile(optimizer=SGD(lr=1e-4, momentum=0.9),
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])
    csv_logger = CSVLogger(output_dir + 'top_model_training.csv')
    model.fit(train_features, train_labels,
              epochs=top_epochs,
              batch_size=batch_size,
              validation_data=(validation_features, validation_labels),
              callbacks=[csv_logger])
    model.save_weights(top_model_weights_path)