Python layers.Reshape方法代码示例

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def create_model(trainable=False):
    model = MobileNetV2(input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3), include_top=False, alpha=ALPHA)

    # to freeze layers
    for layer in model.layers:
        layer.trainable = trainable

    out = model.layers[-1].output

    x = Conv2D(4, kernel_size=3)(out)
    x = Reshape((4,), name="coords")(x)

    y = GlobalAveragePooling2D()(out)
    y = Dense(CLASSES, name="classes", activation="softmax")(y)

    return Model(inputs=model.input, outputs=[x, y]) 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def expand_tile(units, axis):
    """
    Expand and tile tensor along given axis

    Args:
        units: tf tensor with dimensions [batch_size, time_steps, n_input_features]
        axis: axis along which expand and tile. Must be 1 or 2

    """
    assert axis in (1, 2)
    n_time_steps = K.int_shape(units)[1]
    repetitions = [1, 1, 1, 1]
    repetitions[axis] = n_time_steps
    if axis == 1:
        expanded = Reshape(target_shape=((1,) + K.int_shape(units)[1:]))(units)
    else:
        expanded = Reshape(target_shape=(K.int_shape(units)[1:2] + (1,) + K.int_shape(units)[2:]))(units)
    return K.tile(expanded, repetitions) 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def get_aggregation_gate(in_filters, reduction=16):
    """Get the "aggregation gate (AG)" op.

    # Arguments
        reduction: channel reduction for the hidden layer.

    # Returns
        The AG op (a models.Sequential module).
    """
    gate = models.Sequential()
    gate.add(layers.GlobalAveragePooling2D())
    gate.add(layers.Dense(in_filters // reduction, use_bias=False))
    gate.add(layers.BatchNormalization())
    gate.add(layers.Activation('relu'))
    gate.add(layers.Dense(in_filters))
    gate.add(layers.Activation('sigmoid'))
    gate.add(layers.Reshape((1, 1, -1)))  # reshape as (H, W, C)
    return gate 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def create_model(self):
        print('[ImgDecoder] Starting create_model')
        dense = Dense(units=1024, name='p_img_dense')
        reshape = Reshape((1, 1, 1024))

        # for 64x64 img
        deconv1 = Conv2DTranspose(filters=128, kernel_size=4, strides=1, padding='valid', activation='relu')
        deconv2 = Conv2DTranspose(filters=64, kernel_size=5, strides=1, padding='valid', activation='relu', dilation_rate=3)
        deconv3 = Conv2DTranspose(filters=64, kernel_size=6, strides=1, padding='valid', activation='relu', dilation_rate=2)
        deconv4 = Conv2DTranspose(filters=32, kernel_size=5, strides=2, padding='valid', activation='relu', dilation_rate=1)
        deconv5 = Conv2DTranspose(filters=16, kernel_size=5, strides=1, padding='valid', activation='relu', dilation_rate=1)
        # deconv6 = Conv2DTranspose(filters=8, kernel_size=6, strides=2, padding='valid', activation='relu')
        deconv7 = Conv2DTranspose(filters=3, kernel_size=6, strides=1, padding='valid', activation='tanh')
        self.network = tf.keras.Sequential([
            dense,
            reshape,
            deconv1,
            deconv2,
            deconv3,
            deconv4,
            deconv5,
            deconv7], 
            name='p_img')

        print('[ImgDecoder] Done with create_model') 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def _se_block(inputs, filters, se_ratio, prefix):
    x = GlobalAveragePooling2D(name=prefix + 'squeeze_excite/AvgPool')(inputs)
    if K.image_data_format() == 'channels_first':
        x = Reshape((filters, 1, 1))(x)
    else:
        x = Reshape((1, 1, filters))(x)
    x = Conv2D(_depth(filters * se_ratio),
                      kernel_size=1,
                      padding='same',
                      name=prefix + 'squeeze_excite/Conv')(x)
    x = ReLU(name=prefix + 'squeeze_excite/Relu')(x)
    x = Conv2D(filters,
                      kernel_size=1,
                      padding='same',
                      name=prefix + 'squeeze_excite/Conv_1')(x)
    x = Activation(hard_sigmoid)(x)
    #if K.backend() == 'theano':
        ## For the Theano backend, we have to explicitly make
        ## the excitation weights broadcastable.
        #x = Lambda(
            #lambda br: K.pattern_broadcast(br, [True, True, True, False]),
            #output_shape=lambda input_shape: input_shape,
            #name=prefix + 'squeeze_excite/broadcast')(x)
    x = Multiply(name=prefix + 'squeeze_excite/Mul')([inputs, x])
    return x 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def test_compute_model_performance_multitask_classifier(self):
    n_data_points = 20
    n_features = 1
    n_tasks = 2
    n_classes = 2

    X = np.ones(shape=(n_data_points // 2, n_features)) * -1
    X1 = np.ones(shape=(n_data_points // 2, n_features))
    X = np.concatenate((X, X1))
    class_1 = np.array([[0.0, 1.0] for x in range(int(n_data_points / 2))])
    class_0 = np.array([[1.0, 0.0] for x in range(int(n_data_points / 2))])
    y1 = np.concatenate((class_0, class_1))
    y2 = np.concatenate((class_1, class_0))
    y = np.stack([y1, y2], axis=1)
    dataset = NumpyDataset(X, y)

    features = layers.Input(shape=(n_data_points // 2, n_features))
    dense = layers.Dense(n_tasks * n_classes)(features)
    logits = layers.Reshape((n_tasks, n_classes))(dense)
    output = layers.Softmax()(logits)
    keras_model = tf.keras.Model(inputs=features, outputs=[output, logits])
    model = dc.models.KerasModel(
        keras_model,
        dc.models.losses.SoftmaxCrossEntropy(),
        output_types=['prediction', 'loss'],
        learning_rate=0.01,
        batch_size=n_data_points)

    model.fit(dataset, nb_epoch=1000)
    metric = dc.metrics.Metric(
        dc.metrics.roc_auc_score, np.mean, mode="classification")

    scores = model.evaluate_generator(
        model.default_generator(dataset), [metric], per_task_metrics=True)
    scores = list(scores[1].values())
    # Loosening atol to see if tests stop failing sporadically
    assert np.all(np.isclose(scores, [1.0, 1.0], atol=0.50)) 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def _build_graph(self):
    smile_images = Input(shape=self.input_shape)
    stem = chemnet_layers.Stem(self.base_filters)(smile_images)

    inceptionA_out = self.build_inception_module(inputs=stem, type="A")
    reductionA_out = chemnet_layers.ReductionA(
        self.base_filters)(inceptionA_out)

    inceptionB_out = self.build_inception_module(
        inputs=reductionA_out, type="B")
    reductionB_out = chemnet_layers.ReductionB(
        self.base_filters)(inceptionB_out)

    inceptionC_out = self.build_inception_module(
        inputs=reductionB_out, type="C")
    avg_pooling_out = GlobalAveragePooling2D()(inceptionC_out)

    if self.mode == "classification":
      logits = Dense(self.n_tasks * self.n_classes)(avg_pooling_out)
      logits = Reshape((self.n_tasks, self.n_classes))(logits)
      if self.n_classes == 2:
        output = Activation(activation='sigmoid')(logits)
        loss = SigmoidCrossEntropy()
      else:
        output = Softmax()(logits)
        loss = SoftmaxCrossEntropy()
      outputs = [output, logits]
      output_types = ['prediction', 'loss']

    else:
      output = Dense(self.n_tasks * 1)(avg_pooling_out)
      output = Reshape((self.n_tasks, 1))(output)
      outputs = [output]
      output_types = ['prediction']
      loss = L2Loss()

    model = tf.keras.Model(inputs=[smile_images], outputs=outputs)
    return model, loss, output_types 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def squeeze_excite_block(input_tensor, ratio=16):
    """ Create a channel-wise squeeze-excite block

    Args:
        input_tensor: input Keras tensor
        ratio: number of output filters

    Returns: a Keras tensor

    References
    -   [Squeeze and Excitation Networks](https://arxiv.org/abs/1709.01507)
    """
    init = input_tensor
    channel_axis = 1 if K.image_data_format() == "channels_first" else -1
    filters = _tensor_shape(init)[channel_axis]
    se_shape = (1, 1, filters)

    se = GlobalAveragePooling2D()(init)
    se = Reshape(se_shape)(se)
    se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
    se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)

    if K.image_data_format() == 'channels_first':
        se = Permute((3, 1, 2))(se)

    x = multiply([init, se])
    return x 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def create_model(trainable=False):
    model = MobileNetV2(input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3), include_top=False, alpha=ALPHA)

    # to freeze layers
    for layer in model.layers:
        layer.trainable = trainable

    x = model.layers[-1].output
    x = Conv2D(4, kernel_size=3, name="coords")(x)
    x = Reshape((4,))(x)

    return Model(inputs=model.input, outputs=x) 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def CAE(input_shape=(28, 28, 1), filters=[32, 64, 128, 10]):
    model = Sequential()
    if input_shape[0] % 8 == 0:
        pad3 = 'same'
    else:
        pad3 = 'valid'

    model.add(InputLayer(input_shape))
    model.add(Conv2D(filters[0], 5, strides=2, padding='same', activation='relu', name='conv1'))

    model.add(Conv2D(filters[1], 5, strides=2, padding='same', activation='relu', name='conv2'))

    model.add(Conv2D(filters[2], 3, strides=2, padding=pad3, activation='relu', name='conv3'))

    model.add(Flatten())
    model.add(Dense(units=filters[3], name='embedding'))
    model.add(Dense(units=filters[2]*int(input_shape[0]/8)*int(input_shape[0]/8), activation='relu'))

    model.add(Reshape((int(input_shape[0]/8), int(input_shape[0]/8), filters[2])))
    model.add(Conv2DTranspose(filters[1], 3, strides=2, padding=pad3, activation='relu', name='deconv3'))

    model.add(Conv2DTranspose(filters[0], 5, strides=2, padding='same', activation='relu', name='deconv2'))

    model.add(Conv2DTranspose(input_shape[2], 5, strides=2, padding='same', name='deconv1'))
    encoder = Model(inputs=model.input, outputs=model.get_layer('embedding').output)
    return model, encoder 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def __init__(self, batch_input_shape=(None, NUM_FRAMES, NUM_FBANKS, 1), include_softmax=False,
                 num_speakers_softmax=None):
        self.include_softmax = include_softmax
        if self.include_softmax:
            assert num_speakers_softmax > 0
        self.clipped_relu_count = 0

        # http://cs231n.github.io/convolutional-networks/
        # conv weights
        # #params = ks * ks * nb_filters * num_channels_input

        # Conv128-s
        # 5*5*128*128/2+128
        # ks*ks*nb_filters*channels/strides+bias(=nb_filters)

        # take 100 ms -> 4 frames.
        # if signal is 3 seconds, then take 100ms per 100ms and average out this network.
        # 8*8 = 64 features.

        # used to share all the layers across the inputs

        # num_frames = K.shape() - do it dynamically after.
        inputs = Input(batch_shape=batch_input_shape, name='input')
        x = self.cnn_component(inputs)

        x = Reshape((-1, 2048))(x)
        # Temporal average layer. axis=1 is time.
        x = Lambda(lambda y: K.mean(y, axis=1), name='average')(x)
        if include_softmax:
            logger.info('Including a Dropout layer to reduce overfitting.')
            # used for softmax because the dataset we pre-train on might be too small. easy to overfit.
            x = Dropout(0.5)(x)
        x = Dense(512, name='affine')(x)
        if include_softmax:
            # Those weights are just when we train on softmax.
            x = Dense(num_speakers_softmax, activation='softmax')(x)
        else:
            # Does not contain any weights.
            x = Lambda(lambda y: K.l2_normalize(y, axis=1), name='ln')(x)
        self.m = Model(inputs, x, name='ResCNN') 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def build_model(board_size=4, board_layers=16, outputs=4, filters=64, residual_blocks=4):
  # Functional API model
  inputs = layers.Input(shape=(board_size * board_size * board_layers,))
  x = layers.Reshape((board_size, board_size, board_layers))(inputs)

  # Initial convolutional block
  x = layers.Conv2D(filters=filters, kernel_size=(3, 3), padding='same')(x)
  x = layers.BatchNormalization()(x)
  x = layers.Activation('relu')(x)

  # residual blocks
  for i in range(residual_blocks):
    # x at the start of a block
    temp_x = layers.Conv2D(filters=filters, kernel_size=(3, 3), padding='same')(x)
    temp_x = layers.BatchNormalization()(temp_x)
    temp_x = layers.Activation('relu')(temp_x)
    temp_x = layers.Conv2D(filters=filters, kernel_size=(3, 3), padding='same')(temp_x)
    temp_x = layers.BatchNormalization()(temp_x)
    x = layers.add([x, temp_x])
    x = layers.Activation('relu')(x)

  # policy head
  x = layers.Conv2D(filters=2, kernel_size=(1, 1), padding='same')(x)
  x = layers.BatchNormalization()(x)
  x = layers.Activation('relu')(x)
  x = layers.Flatten()(x)
  predictions = layers.Dense(outputs, activation='softmax')(x)

  # Create model
  return models.Model(inputs=inputs, outputs=predictions) 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def __init__(self, width, depth, num_anchors=9, separable_conv=True, freeze_bn=False, detect_quadrangle=False, **kwargs):
        super(BoxNet, self).__init__(**kwargs)
        self.width = width
        self.depth = depth
        self.num_anchors = num_anchors
        self.separable_conv = separable_conv
        self.detect_quadrangle = detect_quadrangle
        num_values = 9 if detect_quadrangle else 4
        options = {
            'kernel_size': 3,
            'strides': 1,
            'padding': 'same',
            'bias_initializer': 'zeros',
        }
        if separable_conv:
            kernel_initializer = {
                'depthwise_initializer': initializers.VarianceScaling(),
                'pointwise_initializer': initializers.VarianceScaling(),
            }
            options.update(kernel_initializer)
            self.convs = [layers.SeparableConv2D(filters=width, name=f'{self.name}/box-{i}', **options) for i in
                          range(depth)]
            self.head = layers.SeparableConv2D(filters=num_anchors * num_values,
                                               name=f'{self.name}/box-predict', **options)
        else:
            kernel_initializer = {
                'kernel_initializer': initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None)
            }
            options.update(kernel_initializer)
            self.convs = [layers.Conv2D(filters=width, name=f'{self.name}/box-{i}', **options) for i in range(depth)]
            self.head = layers.Conv2D(filters=num_anchors * num_values, name=f'{self.name}/box-predict', **options)
        self.bns = [
            [layers.BatchNormalization(momentum=MOMENTUM, epsilon=EPSILON, name=f'{self.name}/box-{i}-bn-{j}') for j in
             range(3, 8)]
            for i in range(depth)]
        # self.bns = [[BatchNormalization(freeze=freeze_bn, name=f'{self.name}/box-{i}-bn-{j}') for j in range(3, 8)]
        #             for i in range(depth)]
        self.relu = layers.Lambda(lambda x: tf.nn.swish(x))
        self.reshape = layers.Reshape((-1, num_values))
        self.level = 0 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def channel_squeeze_excite_block(input, ratio=0.25):
    init = input
    channel_axis = 1 if K.image_data_format() == "channels_first" else -1
    filters = init._keras_shape[channel_axis]
    cse_shape = (1, 1, filters)

    cse = layers.GlobalAveragePooling2D()(init)
    cse = layers.Reshape(cse_shape)(cse)
    ratio_filters = int(np.round(filters * ratio))
    if ratio_filters < 1:
        ratio_filters += 1
    cse = layers.Conv2D(
        ratio_filters,
        (1, 1),
        padding="same",
        activation="relu",
        kernel_initializer="he_normal",
        use_bias=False,
    )(cse)
    cse = layers.BatchNormalization()(cse)
    cse = layers.Conv2D(
        filters,
        (1, 1),
        activation="sigmoid",
        kernel_initializer="he_normal",
        use_bias=False,
    )(cse)

    if K.image_data_format() == "channels_first":
        cse = layers.Permute((3, 1, 2))(cse)

    cse = layers.Multiply()([init, cse])
    return cse 

# 需要导入模块: from tensorflow.keras import layers [as 别名]
# 或者: from tensorflow.keras.layers import Reshape [as 别名]
def _fca_block(inputs, reduct_ratio, block_id):
    in_channels = inputs.shape.as_list()[-1]
    #in_shapes = inputs.shape.as_list()[1:3]
    reduct_channels = int(in_channels // reduct_ratio)
    prefix = 'fca_block_{}_'.format(block_id)
    x = GlobalAveragePooling2D(name=prefix + 'average_pooling')(inputs)
    x = Dense(reduct_channels, activation='relu', name=prefix + 'fc1')(x)
    x = Dense(in_channels, activation='sigmoid', name=prefix + 'fc2')(x)

    x = Reshape((1,1,in_channels),name='reshape')(x)
    x = Multiply(name=prefix + 'multiply')([x, inputs])
    return x