net.py 文件源码

def main(_):
    pp.pprint(flags.FLAGS.__flags)
    sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 0)
    if not os.path.isdir(FLAGS.checkpoint):
        os.mkdir(FLAGS.checkpoint)
    if not os.path.isdir(FLAGS.log):
        os.mkdir(FLAGS.log)
    model = genChipModel()
    model.summary()

    opt = keras.optimizers.rmsprop(lr=0.001, decay=1e-6)
    model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])#'categorical_crossentropy', metrics=['accuracy'])

    filename = '../../data/finalData.txt'   
    x, y = readData(filename)
    x_train, y_train, x_test, y_test = init(x, y)

    y_train_labels = to_categorical(y_train, num_classes=79)
    y_test_labels = to_categorical(y_test, num_classes=79)  
    model_path = os.path.join(FLAGS.checkpoint, "weights.hdf5")
    callbacks = [
        ModelCheckpoint(filepath=model_path, monitor="val_acc", save_best_only=True, save_weights_only=True),
        TensorBoard(log_dir=FLAGS.log),
        ReduceLROnPlateau(monitor='val_acc', factor=0.5, patience=2)
    ]
    hist = model.fit(x_train, y_train_labels, epochs=FLAGS.epoch, batch_size=100, validation_data=(x_test, y_test_labels), callbacks=callbacks)

    loss, accuracy = model.evaluate(x_test, y_test_labels, batch_size=100, verbose=1)