Python keras.utils.vis_utils.plot_model() Examples

The following are 14 code examples of keras.utils.vis_utils.plot_model(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.utils.vis_utils , or try the search function .
Example #1
Source File: helper_functions.py    From fast-neural-style-keras with MIT License 6 votes vote down vote up 
def predict(options, img_read_path, img_write_path):
    # Read image
    content = process_image(img_read_path, -1, -1, resize=False)
    ori_height = content.shape[1]
    ori_width = content.shape[2]

    # Pad image
    content = get_padding(content)
    height = content.shape[1]
    width = content.shape[2]

    # Get eval model
    eval_model = get_evaluate_model(width, height)
    eval_model.load_weights(options['weights_read_path'])

    # If flag is set, print model summary and generate model description
    if options["plot_model"]:
        eval_model.summary()
        plot_model(eval_model, to_file='model.png')

    # Generate output and save image
    res = eval_model.predict([content])
    output = deprocess_image(res[0], width, height)
    output = remove_padding(output, ori_height, ori_width)
    imwrite(img_write_path, output)
Example #2
Source File: fcn.py    From lunania-ai with MIT License 6 votes vote down vote up 
def train(self, dataset, epoches):
        sample_count, generator = createDataGenerater(dataset)
        history = self.model.fit_generator(
            generator(),
            steps_per_epoch=sample_count,
            epochs=epoches,
            verbose=1
        )

        save_dir = join_path(config.model_dir, self.model_type + '-' + str(config.classes) + 'class-' + str(self.epoch) + 'epoch-' + datetime.now().strftime("%Y%m%d%H%M%S"))
        if not os.path.exists(save_dir):
            os.mkdir(save_dir)
        logger.info('model path: %s', save_dir)
        with open(os.path.join(save_dir, self.MODEL_FILE_NAME), mode='w', encoding='utf-8') as model_file:
            model_file.write(self.model.to_yaml())
        vis_utils.plot_model(self.model, to_file=os.path.join(save_dir, self.VISUALIZED_MODEL_FILE_NAME), show_shapes=True)
        self.model.save_weights(os.path.join(save_dir, self.WEIGHTS_FILE_NAME))
        self.model.save(os.path.join(save_dir, self.ALL_IN_MODEL_FILE_NAME))
Example #3
Source File: keras_utils.py    From deep-smoke-machine with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def save_model_figure(model, file_path='/.model.eps'):
    vis_utils.plot_model(model, file_path, show_shapes=True, show_layer_names=True)
Example #4
Source File: keras_utils.py    From timeception with GNU General Public License v3.0 5 votes vote down vote up 
def save_model_figure(model, file_path='/.model.eps'):
    vis_utils.plot_model(model, file_path, show_shapes=True, show_layer_names=True)
Example #5
Source File: classification_bigbench_keras.py    From deepsim with MIT License 5 votes vote down vote up 
def model_summary():
    X_left = Input((dim, dim, bin_vec_dim))
    X_right = Input((dim, dim, bin_vec_dim))
    predictions = classification(X_left, X_right)
    model = Model(inputs=[X_left, X_right], outputs=predictions)
    model.compile(optimizer=K.optimizers.adam(lr=0.0005),
                  loss=K.losses.binary_crossentropy,
                  metrics=['accuracy'])
    
    # plot_model(model, to_file='./result/plot/whole_model.png', show_shapes=True)
Example #6
Source File: vis_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_plot_model():
    model = Sequential()
    model.add(Conv2D(filters=2, kernel_size=(2, 3), input_shape=(3, 5, 5), name='conv'))
    model.add(Flatten(name='flat'))
    model.add(Dense(5, name='dense1'))
    vis_utils.plot_model(model, to_file='model1.png', show_layer_names=False)
    os.remove('model1.png')

    model = Sequential()
    model.add(LSTM(16, return_sequences=True, input_shape=(2, 3), name='lstm'))
    model.add(TimeDistributed(Dense(5, name='dense2')))
    vis_utils.plot_model(model, to_file='model2.png', show_shapes=True)
    os.remove('model2.png')
Example #7
Source File: vis_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_plot_model():
    model = Sequential()
    model.add(Conv2D(filters=2, kernel_size=(2, 3), input_shape=(3, 5, 5), name='conv'))
    model.add(Flatten(name='flat'))
    model.add(Dense(5, name='dense1'))
    vis_utils.plot_model(model, to_file='model1.png', show_layer_names=False)
    os.remove('model1.png')

    model = Sequential()
    model.add(LSTM(16, return_sequences=True, input_shape=(2, 3), name='lstm'))
    model.add(TimeDistributed(Dense(5, name='dense2')))
    vis_utils.plot_model(model, to_file='model2.png', show_shapes=True)
    os.remove('model2.png')
Example #8
Source File: vis_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_plot_model():
    model = Sequential()
    model.add(Conv2D(filters=2, kernel_size=(2, 3), input_shape=(3, 5, 5), name='conv'))
    model.add(Flatten(name='flat'))
    model.add(Dense(5, name='dense1'))
    vis_utils.plot_model(model, to_file='model1.png', show_layer_names=False)
    os.remove('model1.png')

    model = Sequential()
    model.add(LSTM(16, return_sequences=True, input_shape=(2, 3), name='lstm'))
    model.add(TimeDistributed(Dense(5, name='dense2')))
    vis_utils.plot_model(model, to_file='model2.png', show_shapes=True)
    os.remove('model2.png')
Example #9
Source File: vis_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_plot_model():
    model = Sequential()
    model.add(Conv2D(filters=2, kernel_size=(2, 3), input_shape=(3, 5, 5), name='conv'))
    model.add(Flatten(name='flat'))
    model.add(Dense(5, name='dense1'))
    vis_utils.plot_model(model, to_file='model1.png', show_layer_names=False)
    os.remove('model1.png')

    model = Sequential()
    model.add(LSTM(16, return_sequences=True, input_shape=(2, 3), name='lstm'))
    model.add(TimeDistributed(Dense(5, name='dense2')))
    vis_utils.plot_model(model, to_file='model2.png', show_shapes=True)
    os.remove('model2.png')
Example #10
Source File: vis_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_plot_model():
    model = Sequential()
    model.add(Conv2D(filters=2, kernel_size=(2, 3), input_shape=(3, 5, 5), name='conv'))
    model.add(Flatten(name='flat'))
    model.add(Dense(5, name='dense1'))
    vis_utils.plot_model(model, to_file='model1.png', show_layer_names=False)
    os.remove('model1.png')

    model = Sequential()
    model.add(LSTM(16, return_sequences=True, input_shape=(2, 3), name='lstm'))
    model.add(TimeDistributed(Dense(5, name='dense2')))
    vis_utils.plot_model(model, to_file='model2.png', show_shapes=True)
    os.remove('model2.png')
Example #11
Source File: vis_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_plot_model():
    model = Sequential()
    model.add(Conv2D(filters=2, kernel_size=(2, 3), input_shape=(3, 5, 5), name='conv'))
    model.add(Flatten(name='flat'))
    model.add(Dense(5, name='dense1'))
    vis_utils.plot_model(model, to_file='model1.png', show_layer_names=False)
    os.remove('model1.png')

    model = Sequential()
    model.add(LSTM(16, return_sequences=True, input_shape=(2, 3), name='lstm'))
    model.add(TimeDistributed(Dense(5, name='dense2')))
    vis_utils.plot_model(model, to_file='model2.png', show_shapes=True)
    os.remove('model2.png')
Example #12
Source File: vis_utils_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_plot_model():
    model = Sequential()
    model.add(Conv2D(filters=2, kernel_size=(2, 3), input_shape=(3, 5, 5), name='conv'))
    model.add(Flatten(name='flat'))
    model.add(Dense(5, name='dense1'))
    vis_utils.plot_model(model, to_file='model1.png', show_layer_names=False)
    os.remove('model1.png')

    model = Sequential()
    model.add(LSTM(16, return_sequences=True, input_shape=(2, 3), name='lstm'))
    model.add(TimeDistributed(Dense(5, name='dense2')))
    vis_utils.plot_model(model, to_file='model2.png', show_shapes=True)
    os.remove('model2.png')
Example #13
Source File: keras_utils.py    From videograph with GNU General Public License v3.0 5 votes vote down vote up 
def plot_model(model, file_path='model.eps'):
    vis_utils.plot_model(model, file_path, show_shapes=True, show_layer_names=True)
Example #14
Source File: mobilenet_v2.py    From MobileNetV2 with MIT License 4 votes vote down vote up 
def MobileNetv2(input_shape, k, alpha=1.0):
    """MobileNetv2
    This function defines a MobileNetv2 architectures.

    # Arguments
        input_shape: An integer or tuple/list of 3 integers, shape
            of input tensor.
        k: Integer, number of classes.
        alpha: Integer, width multiplier, better in [0.35, 0.50, 0.75, 1.0, 1.3, 1.4].

    # Returns
        MobileNetv2 model.
    """
    inputs = Input(shape=input_shape)

    first_filters = _make_divisible(32 * alpha, 8)
    x = _conv_block(inputs, first_filters, (3, 3), strides=(2, 2))

    x = _inverted_residual_block(x, 16, (3, 3), t=1, alpha=alpha, strides=1, n=1)
    x = _inverted_residual_block(x, 24, (3, 3), t=6, alpha=alpha, strides=2, n=2)
    x = _inverted_residual_block(x, 32, (3, 3), t=6, alpha=alpha, strides=2, n=3)
    x = _inverted_residual_block(x, 64, (3, 3), t=6, alpha=alpha, strides=2, n=4)
    x = _inverted_residual_block(x, 96, (3, 3), t=6, alpha=alpha, strides=1, n=3)
    x = _inverted_residual_block(x, 160, (3, 3), t=6, alpha=alpha, strides=2, n=3)
    x = _inverted_residual_block(x, 320, (3, 3), t=6, alpha=alpha, strides=1, n=1)

    if alpha > 1.0:
        last_filters = _make_divisible(1280 * alpha, 8)
    else:
        last_filters = 1280

    x = _conv_block(x, last_filters, (1, 1), strides=(1, 1))
    x = GlobalAveragePooling2D()(x)
    x = Reshape((1, 1, last_filters))(x)
    x = Dropout(0.3, name='Dropout')(x)
    x = Conv2D(k, (1, 1), padding='same')(x)

    x = Activation('softmax', name='softmax')(x)
    output = Reshape((k,))(x)

    model = Model(inputs, output)
    # plot_model(model, to_file='images/MobileNetv2.png', show_shapes=True)

    return model