获得张量的动态形状

Question

我想在 Keras 中使用 tensorflow 后端创建自定义指标。 假设我们有一个最小化的指标，我们想要获得动态形状的张量的形状。我正在尝试如下操作：

def metric(y_true, y_pred):

    y_num = (tf.shape(y_true))[0]
    K.get_session().run(y_num)

    return anything

但是我总是收到错误消息：

InvalidArgumentError: You must feed a value for placeholder tensor 'conv2d_47_target' with dtype float and shape [?,?,?,?]
     [[Node: conv2d_47_target = Placeholder[dtype=DT_FLOAT, shape=[?,?,?,?], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
     [[Node: metrics/metric/strided_slice/_1079 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_11_metrics/metric/strided_slice", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

我不知道这可能是问题所在。 如果我的代码中有问题，如何仍可以通过其他方式获得形状？ 任何帮助将不胜感激。

更新：

型号代码：

ACTIVATION = "relu"

def convolution_block(x, filters, size, strides=(1,1), padding='same', activation=True):
    x = Conv2D(filters, size, strides=strides, padding=padding)(x)
    x = BatchNormalization()(x)
    if activation == True:
        x = Activation(ACTIVATION)(x)
    return x

def residual_block(blockInput, num_filters=16):
    x = Activation(ACTIVATION)(blockInput)
    x = BatchNormalization()(x)
    x = convolution_block(x, num_filters, (3,3) )
    x = convolution_block(x, num_filters, (3,3), activation=False)
    x = Add()([x, blockInput])
    return x

# Build model
def build_model(input_layer, start_neurons, DropoutRatio = 0.5):
    # 101 -> 50
    conv1 = Conv2D(start_neurons * 1, (3, 3), activation=None, padding="same")(input_layer)
    conv1 = residual_block(conv1,start_neurons * 1)
    conv1 = residual_block(conv1,start_neurons * 1)
    conv1 = Activation(ACTIVATION)(conv1)
    pool1 = MaxPooling2D((2, 2))(conv1)
    pool1 = Dropout(DropoutRatio/2)(pool1)

    # 50 -> 25
    conv2 = Conv2D(start_neurons * 2, (3, 3), activation=None, padding="same")(pool1)
    conv2 = residual_block(conv2,start_neurons * 2)
    conv2 = residual_block(conv2,start_neurons * 2)
    conv2 = Activation(ACTIVATION)(conv2)
    pool2 = MaxPooling2D((2, 2))(conv2)
    pool2 = Dropout(DropoutRatio)(pool2)

    # 25 -> 12
    conv3 = Conv2D(start_neurons * 4, (3, 3), activation=None, padding="same")(pool2)
    conv3 = residual_block(conv3,start_neurons * 4)
    conv3 = residual_block(conv3,start_neurons * 4)
    conv3 = Activation(ACTIVATION)(conv3)
    pool3 = MaxPooling2D((2, 2))(conv3)
    pool3 = Dropout(DropoutRatio)(pool3)

    # 12 -> 6
    conv4 = Conv2D(start_neurons * 8, (3, 3), activation=None, padding="same")(pool3)
    conv4 = residual_block(conv4,start_neurons * 8)
    conv4 = residual_block(conv4,start_neurons * 8)
    conv4 = Activation(ACTIVATION)(conv4)
    pool4 = MaxPooling2D((2, 2))(conv4)
    pool4 = Dropout(DropoutRatio)(pool4)

    # Middle
    convm = Conv2D(start_neurons * 16, (3, 3), activation=None, padding="same")(pool4)
    convm = residual_block(convm,start_neurons * 16)
    convm = residual_block(convm,start_neurons * 16)
    convm = Activation(ACTIVATION)(convm)

    # 6 -> 12
    deconv4 = Conv2DTranspose(start_neurons * 8, (3, 3), strides=(2, 2), padding="same")(convm)
    uconv4 = concatenate([deconv4, conv4])
    uconv4 = Dropout(DropoutRatio)(uconv4)

    uconv4 = Conv2D(start_neurons * 8, (3, 3), activation=None, padding="same")(uconv4)
    uconv4 = residual_block(uconv4,start_neurons * 8)
    uconv4 = residual_block(uconv4,start_neurons * 8)
    uconv4 = Activation(ACTIVATION)(uconv4)

    # 12 -> 25
    #deconv3 = Conv2DTranspose(start_neurons * 4, (3, 3), strides=(2, 2), padding="same")(uconv4)
    deconv3 = Conv2DTranspose(start_neurons * 4, (3, 3), strides=(2, 2), padding="valid")(uconv4)
    uconv3 = concatenate([deconv3, conv3])    
    uconv3 = Dropout(DropoutRatio)(uconv3)

    uconv3 = Conv2D(start_neurons * 4, (3, 3), activation=None, padding="same")(uconv3)
    uconv3 = residual_block(uconv3,start_neurons * 4)
    uconv3 = residual_block(uconv3,start_neurons * 4)
    uconv3 = Activation(ACTIVATION)(uconv3)

    # 25 -> 50
    deconv2 = Conv2DTranspose(start_neurons * 2, (3, 3), strides=(2, 2), padding="same")(uconv3)
    uconv2 = concatenate([deconv2, conv2])

    uconv2 = Dropout(DropoutRatio)(uconv2)
    uconv2 = Conv2D(start_neurons * 2, (3, 3), activation=None, padding="same")(uconv2)
    uconv2 = residual_block(uconv2,start_neurons * 2)
    uconv2 = residual_block(uconv2,start_neurons * 2)
    uconv2 = Activation(ACTIVATION)(uconv2)

    # 50 -> 101
    #deconv1 = Conv2DTranspose(start_neurons * 1, (3, 3), strides=(2, 2), padding="same")(uconv2)
    deconv1 = Conv2DTranspose(start_neurons * 1, (3, 3), strides=(2, 2), padding="valid")(uconv2)
    uconv1 = concatenate([deconv1, conv1])

    uconv1 = Dropout(DropoutRatio)(uconv1)
    uconv1 = Conv2D(start_neurons * 1, (3, 3), activation=None, padding="same")(uconv1)
    uconv1 = residual_block(uconv1,start_neurons * 1)
    uconv1 = residual_block(uconv1,start_neurons * 1)
    uconv1 = Activation(ACTIVATION)(uconv1)

    uconv1 = Dropout(DropoutRatio/2)(uconv1)
    output_layer = Conv2D(1, (1,1), padding="same", activation="sigmoid")(uconv1)
    #output_layer = Conv2D(1, (1,1), padding="same", activation="sigmoid")(uconv1)

    return output_layer