Keras Flatten Layer - 无效参数错误，矩阵未展平？
Keras Flatten Layer - Invalid Argument Error, matrix not flattening?
解释，然后代码，然后输出，然后错误：
看起来展平层没有发挥作用，输出形状取决于批量大小（当我设置 BATCH_SIZE=32 时，[1,32768] 变为 [1,16384]）。我一生都无法理解我做错了什么或如何解决它。我查看了 Flatten 和 Dense 层的 Keras 文档。我也在使用 Tensorflow 后端，这实际上反映在 keras.json 文件中。
这是我的代码：
BATCH_SIZE = 64
EPOCHS = 1000
EPOCH_STEP = 50
vgg = keras.applications.vgg16.VGG16(include_top=False,weights='imagenet',input_shape=(48,48,3))
vgg_input = vgg.inputs
vgg_output = vgg.outputs
#freeze the vgg layers
for layer in vgg.layers:
layer.trainable = False
print('~~~~~~~~~~~~~~~~~~~~~~Tensors~~~~~~~~~~~~~~')
print('vgg_output tensor:')
print(vgg_output)
print()
model_tensor = Flatten()(vgg_output)
print('flattened vgg_output tensor:')
print(model_tensor)
print()
model_tensor = Dense(32, activation='relu')(model_tensor)
print('dense FC flattened vgg_output tensor:')
print(model_tensor)
print('~~~~~~~~~~~~~~~~~~~~~~Tensors~~~~~~~~~~~~~~')
model_tensor = Dense(2, activation='softmax')(model_tensor)
model = Model(inputs=vgg_input,outputs=model_tensor)
print('Model architecture made')
#CHOSEN ARBITRARILY FOR NOW
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
print('Model Compiled')
print(model.summary())
#train top model
val_batch, val_labels = dataGenerator.generateDataBatch(256)
print('validation batch loaded')
batch, labels = dataGenerator.generateDataBatch(2048)
print('training batch loaded')
print('t-batch shape: ' + str(batch.shape))
print('t-batch lable shape: ' + str(labels.shape))
model.fit(x=batch,y=labels,batch_size=BATCH_SIZE,epochs=EPOCHS,verbose=2,validati on_data=(val_batch,val_labels),shuffle=True)
打印输出：
Printed Info:
~~~~~~~~~~~~~~~~~~~~~~Tensors~~~~~~~~~~~~~~
vgg_output tensor:
[<tf.Tensor 'block5_pool/MaxPool:0' shape=(?, 1, 1, 512) dtype=float32>]
flattened vgg_output tensor:
Tensor("flatten_1/Reshape:0", shape=(?, ?), dtype=float32)
dense FC flattened vgg_output tensor:
Tensor("dense_1/Relu:0", shape=(?, 32), dtype=float32)
~~~~~~~~~~~~~~~~~~~~~~Tensors~~~~~~~~~~~~~~
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 48, 48, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 48, 48, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 48, 48, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 24, 24, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 24, 24, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 24, 24, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 12, 12, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 12, 12, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 12, 12, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 12, 12, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 6, 6, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 6, 6, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 6, 6, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 6, 6, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 3, 3, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 3, 3, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 3, 3, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 3, 3, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 1, 1, 512) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 512) 0
_________________________________________________________________
dense_1 (Dense) (None, 32) 16416
_________________________________________________________________
dense_2 (Dense) (None, 2) 66
=================================================================
Total params: 14,731,170
Trainable params: 16,482
Non-trainable params: 14,714,688
_________________________________________________________________
None
Model Compiled
Model architecture made
Model Compiled
validation batch loaded
training batch loaded
t-batch shape: (2048, 48, 48, 3)
t-batch lable shape: (2048, 2)
错误消息的最后一部分（我可以添加所有内容，但其余部分似乎没有帮助）：
tensorflow.python.framework.errors_impl.InvalidArgumentError: Matrix size-incompatible: In[0]: [1,32768], In[1]: [512,32]
[[Node: dense_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/device:CPU:0"](flatten_1/Reshape, dense_1/kernel/read)]]
编辑：在 model.compile() 之后立即添加 print(model.summary()) 的输出。
Keras 模型的 outputs（和 inputs）属性 returns 模型的（张量）输出列表。您可以在日志中确认这一点：
vgg_output tensor:
[<tf.Tensor 'block5_pool/MaxPool:0' shape=(?, 1, 1, 512) dtype=float32>] <-- this is a list
VGG16 有一个输出（即是一个顺序模型），您需要明确地将这个输出张量（即返回列表的第一个元素）传递到下一层 Flatten：
model_tensor = Flatten()(vgg_output[0]) # pass the first element of output
附带说明一下，如果您想查看训练进度条，请不要将 verbose 参数传递给 fit 方法，或者将其值设为 1（即 verbose=1).
解释，然后代码，然后输出，然后错误：
看起来展平层没有发挥作用，输出形状取决于批量大小（当我设置 BATCH_SIZE=32 时，[1,32768] 变为 [1,16384]）。我一生都无法理解我做错了什么或如何解决它。我查看了 Flatten 和 Dense 层的 Keras 文档。我也在使用 Tensorflow 后端，这实际上反映在 keras.json 文件中。
这是我的代码：
BATCH_SIZE = 64
EPOCHS = 1000
EPOCH_STEP = 50
vgg = keras.applications.vgg16.VGG16(include_top=False,weights='imagenet',input_shape=(48,48,3))
vgg_input = vgg.inputs
vgg_output = vgg.outputs
#freeze the vgg layers
for layer in vgg.layers:
layer.trainable = False
print('~~~~~~~~~~~~~~~~~~~~~~Tensors~~~~~~~~~~~~~~')
print('vgg_output tensor:')
print(vgg_output)
print()
model_tensor = Flatten()(vgg_output)
print('flattened vgg_output tensor:')
print(model_tensor)
print()
model_tensor = Dense(32, activation='relu')(model_tensor)
print('dense FC flattened vgg_output tensor:')
print(model_tensor)
print('~~~~~~~~~~~~~~~~~~~~~~Tensors~~~~~~~~~~~~~~')
model_tensor = Dense(2, activation='softmax')(model_tensor)
model = Model(inputs=vgg_input,outputs=model_tensor)
print('Model architecture made')
#CHOSEN ARBITRARILY FOR NOW
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
print('Model Compiled')
print(model.summary())
#train top model
val_batch, val_labels = dataGenerator.generateDataBatch(256)
print('validation batch loaded')
batch, labels = dataGenerator.generateDataBatch(2048)
print('training batch loaded')
print('t-batch shape: ' + str(batch.shape))
print('t-batch lable shape: ' + str(labels.shape))
model.fit(x=batch,y=labels,batch_size=BATCH_SIZE,epochs=EPOCHS,verbose=2,validati on_data=(val_batch,val_labels),shuffle=True)
打印输出：
Printed Info:
~~~~~~~~~~~~~~~~~~~~~~Tensors~~~~~~~~~~~~~~
vgg_output tensor:
[<tf.Tensor 'block5_pool/MaxPool:0' shape=(?, 1, 1, 512) dtype=float32>]
flattened vgg_output tensor:
Tensor("flatten_1/Reshape:0", shape=(?, ?), dtype=float32)
dense FC flattened vgg_output tensor:
Tensor("dense_1/Relu:0", shape=(?, 32), dtype=float32)
~~~~~~~~~~~~~~~~~~~~~~Tensors~~~~~~~~~~~~~~
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 48, 48, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 48, 48, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 48, 48, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 24, 24, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 24, 24, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 24, 24, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 12, 12, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 12, 12, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 12, 12, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 12, 12, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 6, 6, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 6, 6, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 6, 6, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 6, 6, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 3, 3, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 3, 3, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 3, 3, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 3, 3, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 1, 1, 512) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 512) 0
_________________________________________________________________
dense_1 (Dense) (None, 32) 16416
_________________________________________________________________
dense_2 (Dense) (None, 2) 66
=================================================================
Total params: 14,731,170
Trainable params: 16,482
Non-trainable params: 14,714,688
_________________________________________________________________
None
Model Compiled
Model architecture made
Model Compiled
validation batch loaded
training batch loaded
t-batch shape: (2048, 48, 48, 3)
t-batch lable shape: (2048, 2)
错误消息的最后一部分（我可以添加所有内容，但其余部分似乎没有帮助）：
tensorflow.python.framework.errors_impl.InvalidArgumentError: Matrix size-incompatible: In[0]: [1,32768], In[1]: [512,32]
[[Node: dense_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/device:CPU:0"](flatten_1/Reshape, dense_1/kernel/read)]]
编辑：在 model.compile() 之后立即添加 print(model.summary()) 的输出。
Keras 模型的 outputs（和 inputs）属性 returns 模型的（张量）输出列表。您可以在日志中确认这一点：
vgg_output tensor:
[<tf.Tensor 'block5_pool/MaxPool:0' shape=(?, 1, 1, 512) dtype=float32>] <-- this is a list
VGG16 有一个输出（即是一个顺序模型），您需要明确地将这个输出张量（即返回列表的第一个元素）传递到下一层 Flatten：
model_tensor = Flatten()(vgg_output[0]) # pass the first element of output
附带说明一下，如果您想查看训练进度条，请不要将 verbose 参数传递给 fit 方法，或者将其值设为 1（即 verbose=1).