“`Concatenate`”层需要具有匹配形状的输入。形状：[(无，12，12,128)，(无，12，12，32)，(无，12，12，32)，(无，12，12，64)]

Question

我试图建立一个小型的暹罗网络，模型功能如下：

def faceRecoModel(input_shape):
    """
    Implementation of the Inception model used for FaceNet
    
    Arguments:
    input_shape -- shape of the images of the dataset
    Returns:
    model -- a Model() instance in Keras
    """
        
    # Define the input as a tensor with shape input_shape
    X_input = Input(input_shape)

    # Zero-Padding
    X = ZeroPadding2D((3, 3))(X_input)
    
    # First Block
    X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1')(X)
    X = BatchNormalization(axis = 1, name = 'bn1')(X)
    X = Activation('relu')(X)
    
    # Zero-Padding + MAXPOOL
    X = ZeroPadding2D((1, 1))(X)
    X = MaxPooling2D((3, 3), strides = 2)(X)
    
    # Second Block
    X = Conv2D(64, (1, 1), strides = (1, 1), name = 'conv2')(X)
    X = BatchNormalization(axis = 1, epsilon=0.00001, name = 'bn2')(X)
    X = Activation('relu')(X)
    
    # Zero-Padding + MAXPOOL
    X = ZeroPadding2D((1, 1))(X)

    # Second Block
    X = Conv2D(192, (3, 3), strides = (1, 1), name = 'conv3')(X)
    X = BatchNormalization(axis = 1, epsilon=0.00001, name = 'bn3')(X)
    X = Activation('relu')(X)
    
    # Zero-Padding + MAXPOOL
    X = ZeroPadding2D((1, 1))(X)
    X = MaxPooling2D(pool_size = 3, strides = 2)(X)
    
    # Inception 1: a/b/c
    X = inception_block_1a(X)
    X = inception_block_1b(X)
    X = inception_block_1c(X)
    
    # Inception 2: a/b
    X = inception_block_2a(X)
    X = inception_block_2b(X)
    
    # Inception 3: a/b
    X = inception_block_3a(X)
    X = inception_block_3b(X)
    
    # Top layer
    X = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), data_format='channels_first')(X)
    X = Flatten()(X)
    X = Dense(128, name='dense_layer')(X)
    
    # L2 normalization
    X = Lambda(lambda  x: K.l2_normalize(x,axis=1))(X)

    # Create model instance
    model = Model(inputs = X_input, outputs = X, name='FaceRecoModel')
        
    return model

如果我试图以以下方式运行该函数：

network = faceRecoModel(input_shape=(96, 96, 3))

然后将错误显示为：

ValueError                                Traceback (most recent call last)
<ipython-input-25-50d215e607a1> in <module>
----> 1 network = faceRecoModel(input_shape=(96, 96, 3))

~/Documents/inception_block_model.py in faceRecoModel(input_shape)
    276 
    277     # Inception 1: a/b/c
--> 278     X = inception_block_1a(X)
    279     X = inception_block_1b(X)
    280     X = inception_block_1c(X)

~/Documents/inception_block_model.py in inception_block_1a(X)
     66 
     67     # CONCAT
---> 68     inception = concatenate([X_3x3, X_5x5, X_pool, X_1x1], axis=-1)
     69 
     70     return inception

~/.local/lib/python3.7/site-packages/tensorflow/python/keras/layers/merge.py in concatenate(inputs, axis, **kwargs)
    929       A tensor, the concatenation of the inputs alongside axis `axis`.
    930   """
--> 931   return Concatenate(axis=axis, **kwargs)(inputs)
    932 
    933 

~/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
    950     if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
    951       return self._functional_construction_call(inputs, args, kwargs,
--> 952                                                 input_list)
    953 
    954     # Maintains info about the `Layer.call` stack.

~/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
   1089         # Check input assumptions set after layer building, e.g. input shape.
   1090         outputs = self._keras_tensor_symbolic_call(
-> 1091             inputs, input_masks, args, kwargs)
   1092 
   1093         if outputs is None:

~/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
    820       return nest.map_structure(keras_tensor.KerasTensor, output_signature)
    821     else:
--> 822       return self._infer_output_signature(inputs, args, kwargs, input_masks)
    823 
    824   def _infer_output_signature(self, inputs, args, kwargs, input_masks):

~/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks)
    860           # overridden).
    861           # TODO(kaftan): do we maybe_build here, or have we already done it?
--> 862           self._maybe_build(inputs)
    863           outputs = call_fn(inputs, *args, **kwargs)
    864 

~/.local/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py in _maybe_build(self, inputs)
   2708         # operations.
   2709         with tf_utils.maybe_init_scope(self):
-> 2710           self.build(input_shapes)  # pylint:disable=not-callable
   2711       # We must set also ensure that the layer is marked as built, and the build
   2712       # shape is stored since user defined build functions may not be calling

~/.local/lib/python3.7/site-packages/tensorflow/python/keras/utils/tf_utils.py in wrapper(instance, input_shape)
    270     if input_shape is not None:
    271       input_shape = convert_shapes(input_shape, to_tuples=True)
--> 272     output_shape = fn(instance, input_shape)
    273     # Return shapes from `fn` as TensorShapes.
    274     if output_shape is not None:

~/.local/lib/python3.7/site-packages/tensorflow/python/keras/layers/merge.py in build(self, input_shape)
    517             shape[axis] for shape in shape_set if shape[axis] is not None)
    518         if len(unique_dims) > 1:
--> 519           raise ValueError(err_msg)
    520 
    521   def _merge_function(self, inputs):

ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 12, 12, 128), (None, 12, 12, 32), (None, 12, 12, 32), (None, 12, 12, 64)]

我什么都试过了，但似乎找不到这个问题的答案。

显示错误的函数如下：

def inception_block_1a(X):
    """
    Implementation of an inception block
    """
    
    X_3x3 = Conv2D(96, (1, 1), name ='inception_3a_3x3_conv1')(X)
    X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name = 'inception_3a_3x3_bn1')(X_3x3)
    X_3x3 = Activation('relu')(X_3x3)
    X_3x3 = ZeroPadding2D(padding=(1, 1))(X_3x3)
    X_3x3 = Conv2D(128, (3, 3), name='inception_3a_3x3_conv2')(X_3x3)
    X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_3x3_bn2')(X_3x3)
    X_3x3 = Activation('relu')(X_3x3)
    
    X_5x5 = Conv2D(16, (1, 1), name='inception_3a_5x5_conv1')(X)
    X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_5x5_bn1')(X_5x5)
    X_5x5 = Activation('relu')(X_5x5)
    X_5x5 = ZeroPadding2D(padding=(2, 2))(X_5x5)
    X_5x5 = Conv2D(32, (5, 5), name='inception_3a_5x5_conv2')(X_5x5)
    X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_5x5_bn2')(X_5x5)
    X_5x5 = Activation('relu')(X_5x5)

    X_pool = MaxPooling2D(pool_size=3, strides=2)(X)
    X_pool = Conv2D(32, (1, 1), name='inception_3a_pool_conv')(X_pool)
    X_pool = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_pool_bn')(X_pool)
    X_pool = Activation('relu')(X_pool)
    X_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(X_pool)

    X_1x1 = Conv2D(64, (1, 1), name='inception_3a_1x1_conv')(X)
    X_1x1 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_1x1_bn')(X_1x1)
    X_1x1 = Activation('relu')(X_1x1)
        
    # CONCAT
    inception = concatenate([X_3x3, X_5x5, X_pool, X_1x1], axis=-1)
    return inception

Answer 1

正如错误消息所述，tf.keras.layers.concatenate接受具有匹配形状的输入。当输入的形状不同时，使axis=1，如tf.keras.layers.concatenate([x, y],axis=1)。

我可以复制你的问题

import numpy as np
import tensorflow as tf
x = np.arange(20).reshape(2, 2, 5)
y = np.arange(20, 30).reshape(2, 1, 5)
tf.keras.layers.concatenate([x, y],axis=-1)

输出

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-0084e00eb8b5> in <module>()
      4 y = np.arange(20, 30).reshape(2, 1, 5)
      5 tf.keras.layers.concatenate([x, y],
----> 6                             axis=-1)

2 frames
/usr/local/lib/python3.7/dist-packages/keras/layers/merge.py in build(self, input_shape)
    523             shape[axis] for shape in shape_set if shape[axis] is not None)
    524         if len(unique_dims) > 1:
--> 525           raise ValueError(err_msg)
    526 
    527   def _merge_function(self, inputs):

ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concatenation axis. Received: input_shape=[(2, 2, 5), (2, 1, 5)]

工作样例代码

import numpy as np
import tensorflow as tf
x = np.arange(20).reshape(2, 2, 5)
y = np.arange(20, 30).reshape(2, 1, 5)
tf.keras.layers.concatenate([x, y],axis=1)

输出

<tf.Tensor: shape=(2, 3, 5), dtype=int64, numpy=
array([[[ 0,  1,  2,  3,  4],
        [ 5,  6,  7,  8,  9],
        [20, 21, 22, 23, 24]],

       [[10, 11, 12, 13, 14],
        [15, 16, 17, 18, 19],
        [25, 26, 27, 28, 29]]])>