Python applications.VGG19属性代码示例

# 需要导入模块: from keras import applications [as 别名]
# 或者: from keras.applications import VGG19 [as 别名]
def build_vgg(self):
        """
        Builds a pre-trained VGG19 model that outputs image features extracted at the
        third block of the model
        """
        vgg = VGG19(weights="imagenet")
        # Set outputs to outputs of last conv. layer in block 3
        # See architecture at: https://github.com/keras-team/keras/blob/master/keras/applications/vgg19.py
        vgg.outputs = [vgg.layers[9].output]

        img = Input(shape=self.hr_shape)

        # Extract image features
        img_features = vgg(img)

        return Model(img, img_features) 

# 需要导入模块: from keras import applications [as 别名]
# 或者: from keras.applications import VGG19 [as 别名]
def build_vgg():
    """
    Build VGG network to extract image features
    """
    input_shape = (256, 256, 3)

    # Load a pre-trained VGG19 model trained on 'Imagenet' dataset
    vgg = VGG19(weights="imagenet")
    vgg.outputs = [vgg.layers[9].output]

    input_layer = Input(shape=input_shape)

    # Extract features
    features = vgg(input_layer)

    # Create a Keras model
    model = Model(inputs=[input_layer], outputs=[features])
    return model 

# 需要导入模块: from keras import applications [as 别名]
# 或者: from keras.applications import VGG19 [as 别名]
def model_vgg_create(input_shape, num_classes):

    logging.debug('input_shape {}'.format(input_shape))
    #model = applications.VGG19(weights = "imagenet", include_top=False, input_shape = (256, 256, 3))
    model = applications.VGG19(weights = "imagenet", include_top=False, input_shape = (input_shape))        #  input_shape (128, 128, 1)
                                                                                                            #  input_shape (128, 128, 3)

    # Freeze the layers which you don't want to train. Freezing the first 5 layers.
    for layer in model.layers[:5]:
        layer.trainable = False

    # Adding custom Layers
    x = model.output
    x = Flatten()(x)
    x = Dense(1024, activation="relu")(x)
    x = Dropout(0.5)(x)
    x = Dense(1024, activation="relu")(x)
    predictions = Dense(num_classes, activation="softmax")(x)

    # Creating the final model
    model_final = Model(inputs = model.input, outputs = predictions)

    # Compile the model
    # opt = RMSprop(lr=0.0001, decay=1e-6)
    opt = SGD(lr=0.0001, momentum=0.9)
    model_final.compile(loss = "categorical_crossentropy", optimizer = opt, metrics=["accuracy"])

    return model_final 

# 需要导入模块: from keras import applications [as 别名]
# 或者: from keras.applications import VGG19 [as 别名]
def setup_model(encoder, layer_name):
    image_input = Input(shape=(224, 224, 3))

    base_model = None
    if encoder == 'vgg16':
        base_model = VGG16(include_top=False, weights='imagenet', input_tensor=image_input, input_shape=(224, 224, 3))
    elif encoder == 'vgg19':
        base_model = VGG19(include_top=False, weights='imagenet', input_tensor=image_input, input_shape=(224, 224, 3))
    else:
        raise ValueError("not implemented encoder type")

    model = Model(inputs=base_model.input, outputs=base_model.get_layer(layer_name).output)
    return model 

# 需要导入模块: from keras import applications [as 别名]
# 或者: from keras.applications import VGG19 [as 别名]
def test_vgg():
    app = random.choice([applications.VGG16, applications.VGG19])
    last_dim = 512
    _test_application_basic(app)
    _test_application_notop(app, last_dim)
    _test_application_variable_input_channels(app, last_dim)
    _test_app_pooling(app, last_dim) 

# 需要导入模块: from keras import applications [as 别名]
# 或者: from keras.applications import VGG19 [as 别名]
def build_model():
    import keras.applications as kapp
    from keras.layers import Input
    from keras.backend import floatx
    inputLayer = Input(shape=(224, 224, 3), dtype=floatx())
    return kapp.VGG19(input_tensor=inputLayer) 

# 需要导入模块: from keras import applications [as 别名]
# 或者: from keras.applications import VGG19 [as 别名]
def get_imagenet_architecture(architecture, variant, size, alpha, output_layer, include_top=False, weights='imagenet'):
    from keras import applications, Model

    if include_top:
        assert output_layer == 'last'

    if size == 'auto':
        size = get_image_size(architecture, variant, size)

    shape = (size, size, 3)

    if architecture == 'densenet':
        if variant == 'auto':
            variant = 'densenet-121'
        if variant == 'densenet-121':
            model = applications.DenseNet121(weights=weights, include_top=include_top, input_shape=shape)
        elif variant == 'densenet-169':
            model = applications.DenseNet169(weights=weights, include_top=include_top, input_shape=shape)
        elif variant == 'densenet-201':
            model = applications.DenseNet201(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'inception-resnet-v2':
        model = applications.InceptionResNetV2(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'mobilenet':
        model = applications.MobileNet(weights=weights, include_top=include_top, input_shape=shape, alpha=alpha)
    elif architecture == 'mobilenet-v2':
        model = applications.MobileNetV2(weights=weights, include_top=include_top, input_shape=shape, alpha=alpha)
    elif architecture == 'nasnet':
        if variant == 'auto':
            variant = 'large'
        if variant == 'large':
            model = applications.NASNetLarge(weights=weights, include_top=include_top, input_shape=shape)
        else:
            model = applications.NASNetMobile(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'resnet-50':
        model = applications.ResNet50(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'vgg-16':
        model = applications.VGG16(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'vgg-19':
        model = applications.VGG19(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'xception':
        model = applications.Xception(weights=weights, include_top=include_top, input_shape=shape)
    elif architecture == 'inception-v3':
        model = applications.InceptionV3(weights=weights, include_top=include_top, input_shape=shape)

    if output_layer != 'last':
        try:
            if isinstance(output_layer, int):
                layer = model.layers[output_layer]
            else:
                layer = model.get_layer(output_layer)
        except Exception:
            raise VergeMLError('layer not found: {}'.format(output_layer))
        model = Model(inputs=model.input, outputs=layer.output)

    return model 

# 需要导入模块: from keras import applications [as 别名]
# 或者: from keras.applications import VGG19 [as 别名]
def train(self, epochs, batch_size=1, sample_interval=50):

        start_time = datetime.datetime.now()

        for epoch in range(epochs):

            # ----------------------
            #  Train Discriminator
            # ----------------------

            # Sample images and their conditioning counterparts
            imgs_hr, imgs_lr = self.data_loader.load_data(batch_size)

            # From low res. image generate high res. version
            fake_hr = self.generator.predict(imgs_lr)

            valid = np.ones((batch_size,) + self.disc_patch)
            fake = np.zeros((batch_size,) + self.disc_patch)

            # Train the discriminators (original images = real / generated = Fake)
            d_loss_real = self.discriminator.train_on_batch(imgs_hr, valid)
            d_loss_fake = self.discriminator.train_on_batch(fake_hr, fake)
            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

            # ------------------
            #  Train Generator
            # ------------------

            # Sample images and their conditioning counterparts
            imgs_hr, imgs_lr = self.data_loader.load_data(batch_size)

            # The generators want the discriminators to label the generated images as real
            valid = np.ones((batch_size,) + self.disc_patch)

            # Extract ground truth image features using pre-trained VGG19 model
            image_features = self.vgg.predict(imgs_hr)

            # Train the generators
            g_loss = self.combined.train_on_batch([imgs_lr, imgs_hr], [valid, image_features])

            elapsed_time = datetime.datetime.now() - start_time
            # Plot the progress
            print ("%d time: %s" % (epoch, elapsed_time))

            # If at save interval => save generated image samples
            if epoch % sample_interval == 0:
                self.sample_images(epoch) 

# 需要导入模块: from keras import applications [as 别名]
# 或者: from keras.applications import VGG19 [as 别名]
def __init__(self):
        # Input shape
        self.channels = 3
        self.lr_height = 64                 # Low resolution height
        self.lr_width = 64                  # Low resolution width
        self.lr_shape = (self.lr_height, self.lr_width, self.channels)
        self.hr_height = self.lr_height*4   # High resolution height
        self.hr_width = self.lr_width*4     # High resolution width
        self.hr_shape = (self.hr_height, self.hr_width, self.channels)

        # Number of residual blocks in the generator
        self.n_residual_blocks = 16

        # We use a pre-trained VGG19 model to extract image features from the high resolution
        # and the generated high resolution images and minimize the mse between them
        self.vgg = self.build_vgg()
        self.vgg.trainable = False

        # Calculate output shape of D (PatchGAN)
        patch = int(self.hr_height / 2**4)
        self.disc_patch = (patch, patch, 1)

        # Number of filters in the first layer of G and D
        self.gf = 64
        self.df = 64

        # Build and compile the discriminator
        self.discriminator = self.build_discriminator()

        # Build the generator
        self.generator = self.build_generator()

        # High res. and low res. images
        img_hr = Input(shape=self.hr_shape)
        img_lr = Input(shape=self.lr_shape)

        # Generate high res. version from low res.
        fake_hr = self.generator(img_lr)

        # Extract image features of the generated img
        fake_features = self.vgg(fake_hr)

        # For the combined model we will only train the generator
        self.discriminator.trainable = False

        # Discriminator determines validity of generated high res. images
        validity = self.discriminator(fake_hr)

        self.combined = Model([img_lr, img_hr], [validity, fake_features])