Keras ValueError: Dimensions must be equal issue
Keras ValueError: Dimensions must be equal issue
即使应用了答案和评论中的建议，尺寸不匹配问题似乎仍然存在。这也是要复制的确切代码和数据文件：https://drive.google.com/drive/folders/1q67s0VhB-O7J8OtIhU2jmj7Kc4LxL3sf?usp=sharing
如何纠正！？最新代码、模型摘要、使用的函数和我得到的错误如下
type_ae=='dcor'
#Wrappers for keras
def custom_loss1(y_true,y_pred):
dcor = -1*distance_correlation(y_true,encoded_layer)
return dcor
def custom_loss2(y_true,y_pred):
recon_loss = losses.categorical_crossentropy(y_true, y_pred)
return recon_loss
input_layer = Input(shape=(64,64,1))
encoded_layer = Conv2D(filters = 128, kernel_size = (5,5),padding = 'same',activation ='relu',
input_shape = (64,64,1))(input_layer)
encoded_layer = MaxPool2D(pool_size=(2,2))(encoded_layer)
encoded_layer = Dropout(0.25)(encoded_layer)
encoded_layer = (Conv2D(filters = 64, kernel_size = (3,3),padding = 'same',activation ='relu'))(encoded_layer)
encoded_layer = (MaxPool2D(pool_size=(2,2)))(encoded_layer)
encoded_layer = (Dropout(0.25))(encoded_layer)
encoded_layer = (Conv2D(filters = 64, kernel_size = (3,3),padding = 'same',activation ='relu'))(encoded_layer)
encoded_layer = (MaxPool2D(pool_size=(2,2)))(encoded_layer)
encoded_layer = (Dropout(0.25))(encoded_layer)
encoded_layer = Conv2D(filters = 1, kernel_size = (3,3),padding = 'same',activation ='relu',
input_shape = (64,64,1),strides=1)(encoded_layer)
encoded_layer = ZeroPadding2D(padding=(28, 28), data_format=None)(encoded_layer)
decoded_imag = Conv2D(8, (2, 2), activation='relu', padding='same')(encoded_layer)
decoded_imag = UpSampling2D((2, 2))(decoded_imag)
decoded_imag = Conv2D(8, (3, 3), activation='relu', padding='same')(decoded_imag)
decoded_imag = UpSampling2D((2, 2))(decoded_imag)
decoded_imag = Conv2D(16, (3, 3), activation='relu', padding='same')(decoded_imag)
decoded_imag = UpSampling2D((2, 2))(decoded_imag)
decoded_imag = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(decoded_imag)
flat_layer = Flatten()(decoded_imag)
dense_layer = Dense(256,activation = "relu")(flat_layer)
dense_layer = Dense(64,activation = "relu")(dense_layer)
dense_layer = Dense(32,activation = "relu")(dense_layer)
output_layer = Dense(9, activation = "softmax")(dense_layer)
autoencoder = Model(input_layer, [encoded_layer,output_layer])
autoencoder.summary()
autoencoder.compile(optimizer='adadelta', loss=[custom_loss1,custom_loss2])
autoencoder.fit(x_train,[x_train, y_train],batch_size=32,epochs=3,shuffle=True,
validation_data=(x_val, [x_val,y_val]))
数据维度：
x_train.shape: (4000, 64, 64, 1)
x_val.shape: (1000, 64, 64, 1)
y_train.shape: (4000, 9)
y_val.shape: (1000, 9)
损失看起来像：
def custom_loss1(y_true,y_pred):
dcor = -1*distance_correlation(y_true,encoded_layer)
return dcor
def custom_loss2(y_true,y_pred):
recon_loss = losses.categorical_crossentropy(y_true, y_pred)
return recon_loss
相关函数基于张量如下：
def distance_correlation(y_true,y_pred):
pred_r = tf.reduce_sum(y_pred*y_pred,1)
pred_r = tf.reshape(pred_r,[-1,1])
pred_d = pred_r - 2*tf.matmul(y_pred,tf.transpose(y_pred))+tf.transpose(pred_r)
true_r = tf.reduce_sum(y_true*y_true,1)
true_r = tf.reshape(true_r,[-1,1])
true_d = true_r - 2*tf.matmul(y_true,tf.transpose(y_true))+tf.transpose(true_r)
concord = 1-tf.matmul(y_true,tf.transpose(y_true))
#print(pred_d)
#print(tf.reshape(tf.reduce_mean(pred_d,1),[-1,1]))
#print(tf.reshape(tf.reduce_mean(pred_d,0),[1,-1]))
#print(tf.reduce_mean(pred_d))
tf.check_numerics(pred_d,'pred_d has NaN')
tf.check_numerics(true_d,'true_d has NaN')
A = pred_d - tf.reshape(tf.reduce_mean(pred_d,1),[-1,1]) - tf.reshape(tf.reduce_mean(pred_d,0),[1,-1]) + tf.reduce_mean(pred_d)
B = true_d - tf.reshape(tf.reduce_mean(true_d,1),[-1,1]) - tf.reshape(tf.reduce_mean(true_d,0),[1,-1]) + tf.reduce_mean(true_d)
#dcor = -tf.reduce_sum(concord*pred_d)/tf.reduce_sum((1-concord)*pred_d)
dcor = -tf.log(tf.reduce_mean(A*B))+tf.log(tf.sqrt(tf.reduce_mean(A*A)*tf.reduce_mean(B*B)))#-tf.reduce_sum(concord*pred_d)/tf.reduce_sum((1-concord)*pred_d)
#print(dcor.shape)
#tf.Print(dcor,[dcor])
#dcor = tf.tile([dcor],batch_size)
return (dcor)
模型摘要如下：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) (None, 64, 64, 1) 0
_________________________________________________________________
conv2d_30 (Conv2D) (None, 64, 64, 128) 3328
_________________________________________________________________
max_pooling2d_13 (MaxPooling (None, 32, 32, 128) 0
_________________________________________________________________
dropout_13 (Dropout) (None, 32, 32, 128) 0
_________________________________________________________________
conv2d_31 (Conv2D) (None, 32, 32, 64) 73792
_________________________________________________________________
max_pooling2d_14 (MaxPooling (None, 16, 16, 64) 0
_________________________________________________________________
dropout_14 (Dropout) (None, 16, 16, 64) 0
_________________________________________________________________
conv2d_32 (Conv2D) (None, 16, 16, 64) 36928
_________________________________________________________________
max_pooling2d_15 (MaxPooling (None, 8, 8, 64) 0
_________________________________________________________________
dropout_15 (Dropout) (None, 8, 8, 64) 0
_________________________________________________________________
conv2d_33 (Conv2D) (None, 8, 8, 1) 577
_________________________________________________________________
zero_padding2d_5 (ZeroPaddin (None, 64, 64, 1) 0
_________________________________________________________________
conv2d_34 (Conv2D) (None, 64, 64, 8) 40
_________________________________________________________________
up_sampling2d_10 (UpSampling (None, 128, 128, 8) 0
_________________________________________________________________
conv2d_35 (Conv2D) (None, 128, 128, 8) 584
_________________________________________________________________
up_sampling2d_11 (UpSampling (None, 256, 256, 8) 0
_________________________________________________________________
conv2d_36 (Conv2D) (None, 256, 256, 16) 1168
_________________________________________________________________
up_sampling2d_12 (UpSampling (None, 512, 512, 16) 0
_________________________________________________________________
conv2d_37 (Conv2D) (None, 512, 512, 1) 145
_________________________________________________________________
flatten_4 (Flatten) (None, 262144) 0
_________________________________________________________________
dense_13 (Dense) (None, 256) 67109120
_________________________________________________________________
dense_14 (Dense) (None, 64) 16448
_________________________________________________________________
dense_15 (Dense) (None, 32) 2080
_________________________________________________________________
dense_16 (Dense) (None, 9) 297
=================================================================
Total params: 67,244,507
Trainable params: 67,244,507
Non-trainable params: 0
_________________________________________________________________
这是错误：
InvalidArgumentError Traceback (most recent call last)
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1658 try:
-> 1659 c_op = c_api.TF_FinishOperation(op_desc)
1660 except errors.InvalidArgumentError as e:
InvalidArgumentError: Dimensions must be equal, but are 1 and 64 for 'loss_1/zero_padding2d_5_loss/MatMul' (op: 'BatchMatMul') with input shapes: [?,64,64,1], [1,64,64,?].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-11-0e924885fc6b> in <module>
40 autoencoder = Model(input_layer, [encoded_layer,output_layer])
41 autoencoder.summary()
---> 42 autoencoder.compile(optimizer='adadelta', loss=[custom_loss1,custom_loss2])
43 autoencoder.fit(x_train,[x_train, y_train],batch_size=32,epochs=3,shuffle=True,
44 validation_data=(x_val, [x_val,y_val]))
~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, **kwargs)
340 with K.name_scope(self.output_names[i] + '_loss'):
341 output_loss = weighted_loss(y_true, y_pred,
--> 342 sample_weight, mask)
343 if len(self.outputs) > 1:
344 self.metrics_tensors.append(output_loss)
~/anaconda3/lib/python3.6/site-packages/keras/engine/training_utils.py in weighted(y_true, y_pred, weights, mask)
402 """
403 # score_array has ndim >= 2
--> 404 score_array = fn(y_true, y_pred)
405 if mask is not None:
406 # Cast the mask to floatX to avoid float64 upcasting in Theano
<ipython-input-11-0e924885fc6b> in custom_loss1(y_true, y_pred)
2 #Wrappers for keras
3 def custom_loss1(y_true,y_pred):
----> 4 dcor = -1*distance_correlation(y_true,encoded_layer)
5 return dcor
6
<ipython-input-6-f282528532cc> in distance_correlation(y_true, y_pred)
2 pred_r = tf.reduce_sum(y_pred*y_pred,1)
3 pred_r = tf.reshape(pred_r,[-1,1])
----> 4 pred_d = pred_r - 2*tf.matmul(y_pred,tf.transpose(y_pred))+tf.transpose(pred_r)
5 true_r = tf.reduce_sum(y_true*y_true,1)
6 true_r = tf.reshape(true_r,[-1,1])
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py in matmul(a, b, transpose_a, transpose_b, adjoint_a, adjoint_b, a_is_sparse, b_is_sparse, name)
2415 adjoint_b = True
2416 return gen_math_ops.batch_mat_mul(
-> 2417 a, b, adj_x=adjoint_a, adj_y=adjoint_b, name=name)
2418
2419 # Neither matmul nor sparse_matmul support adjoint, so we conjugate
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py in batch_mat_mul(x, y, adj_x, adj_y, name)
1421 adj_y = _execute.make_bool(adj_y, "adj_y")
1422 _, _, _op = _op_def_lib._apply_op_helper(
-> 1423 "BatchMatMul", x=x, y=y, adj_x=adj_x, adj_y=adj_y, name=name)
1424 _result = _op.outputs[:]
1425 _inputs_flat = _op.inputs
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
786 op = g.create_op(op_type_name, inputs, output_types, name=scope,
787 input_types=input_types, attrs=attr_protos,
--> 788 op_def=op_def)
789 return output_structure, op_def.is_stateful, op
790
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs)
505 'in a future version' if date is None else ('after %s' % date),
506 instructions)
--> 507 return func(*args, **kwargs)
508
509 doc = _add_deprecated_arg_notice_to_docstring(
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in create_op(***failed resolving arguments***)
3298 input_types=input_types,
3299 original_op=self._default_original_op,
-> 3300 op_def=op_def)
3301 self._create_op_helper(ret, compute_device=compute_device)
3302 return ret
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1821 op_def, inputs, node_def.attr)
1822 self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
-> 1823 control_input_ops)
1824
1825 # Initialize self._outputs.
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1660 except errors.InvalidArgumentError as e:
1661 # Convert to ValueError for backwards compatibility.
-> 1662 raise ValueError(str(e))
1663
1664 return c_op
ValueError: Dimensions must be equal, but are 1 and 64 for 'loss_1/zero_padding2d_5_loss/MatMul' (op: 'BatchMatMul') with input shapes: [?,64,64,1], [1,64,64,?].
您有两个损失函数，因此您必须传递两个 y（基本事实）来评估与预测相关的损失。
您的第一个预测是 encoded_layer 层的输出，从 conv2d_59 (Conv2D)
的 model.summary 观察到的大小为 (None, 8, 8, 128)
但是您为 y 传递的是 [x_train, y_train]。 loss_1 期望输入大小为 (None, 8, 8, 128) 但您传递的 x_train 具有不同的大小。
如果您希望 loss_1 找到输入图像与编码图像的相关性，然后堆叠卷积，这样卷积的输出将产生与您的形状相同的形状 x_train 图像形状。使用 model.summary 查看卷积的输出形状。
不使用卷积层的填充、步幅和内核大小来获得所需的卷积输出大小。使用公式 W2=(W1−F+2P)/S+1 和 H2=(H1−F+2P)/S+1 求出卷积的输出宽度和高度。检查这个 reference
您的方法有两个主要问题。
- 你的损失函数正在检查编码图像和实际图像之间的相关性。正确的做法是从编码图像解码回图像，然后检查解码图像和实际图像之间的相关性（在自动编码器的行中）
- 你的损失 1 使用了 numpy 数组。对于要成为计算图的一部分的损失函数，它应该使用张量运算，而不是 numy 运算。
下面是工作代码。但是，对于损失 1，我使用了两个图像的 l2 范数。如果你想使用相关性，那么你必须以某种方式将它转换为张量运算（这与这个问题不同）
def image_loss(y_true,y_pred):
return tf.norm(y_true - y_pred)
def label_loss(y_true,y_pred):
return categorical_crossentropy(y_true, y_pred)
input_img = Input(shape=(64, 64, 1))
enocded_imag = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
enocded_imag = MaxPooling2D((2, 2), padding='same')(enocded_imag)
enocded_imag = Conv2D(8, (3, 3), activation='relu', padding='same')(enocded_imag)
enocded_imag = MaxPooling2D((2, 2), padding='same')(enocded_imag)
enocded_imag = Conv2D(8, (3, 3), activation='relu', padding='same')(enocded_imag)
enocded_imag = MaxPooling2D((2, 2), padding='same')(enocded_imag)
decoded_imag = Conv2D(8, (2, 2), activation='relu', padding='same')(enocded_imag)
decoded_imag = UpSampling2D((2, 2))(decoded_imag)
decoded_imag = Conv2D(8, (3, 3), activation='relu', padding='same')(decoded_imag)
decoded_imag = UpSampling2D((2, 2))(decoded_imag)
decoded_imag = Conv2D(16, (3, 3), activation='relu', padding='same')(decoded_imag)
decoded_imag = UpSampling2D((2, 2))(decoded_imag)
decoded_imag = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(decoded_imag)
flat_layer = Flatten()(enocded_imag)
dense_layer = Dense(32,activation = "relu")(flat_layer)
output_layer = Dense(9, activation = "softmax")(dense_layer)
model = Model(input_img, [decoded_imag, output_layer])
model.compile(optimizer='adadelta', loss=[image_loss, label_loss])
images = np.random.randn(10,64,64,1)
model.fit(images, [images, np.random.randn(10,9)])
您编写的损失函数 distance_correlation 假定 y_true 和 y_pred 中的每一行代表一个图像。当您使用 Dense 层时，它会起作用，因为 Dense 层输出一批（行）向量，其中每个向量代表一个单独的图像。但是，二维卷积对具有多个通道的一批二维张量进行操作（您只有一个通道）。因此，要使用 distance_correlation 损失函数，您必须重塑张量，使每一行对应一个图像。添加以下两行以重塑您的张量。
def distance_correlation(y_true,y_pred):
y_true = tf.reshape(tf.squeeze(y_true), [-1,64*64])
y_pred = tf.reshape(tf.squeeze(y_pred), [-1,64*64])
.... REST OF THE CODE ....
目的是使用custom_loss1中的原始图像和custom_loss2中的标量标签值。我认为@mujjiga 在他的回答中的工作代码几乎是正确的。我建议稍微修改一下。
在model.compile()中传递需要它的损失中的输入张量。保持另一个不变。 model.fit() 只是传递标签。
model.compile(optimizer='adadelta', loss=[custom_loss1(input_layer), custom_loss2])
mode.fit(x_train, y_train)
自定义损失函数内部：
def custom_loss1(input):
def loss1(y_true, y_pred):
return tf.norm(input - y_pred) # use your custom loss 1
return loss1
def custom_loss2(y_true, y_pred):
return categorical_crossentropy(y_true, y_pred) # use your custom loss 2
先用简单的 in-built Keras 损失函数试试这个。如果效果不错，请查看您的自定义损失函数。
即使应用了答案和评论中的建议，尺寸不匹配问题似乎仍然存在。这也是要复制的确切代码和数据文件：https://drive.google.com/drive/folders/1q67s0VhB-O7J8OtIhU2jmj7Kc4LxL3sf?usp=sharing
如何纠正！？最新代码、模型摘要、使用的函数和我得到的错误如下
type_ae=='dcor'
#Wrappers for keras
def custom_loss1(y_true,y_pred):
dcor = -1*distance_correlation(y_true,encoded_layer)
return dcor
def custom_loss2(y_true,y_pred):
recon_loss = losses.categorical_crossentropy(y_true, y_pred)
return recon_loss
input_layer = Input(shape=(64,64,1))
encoded_layer = Conv2D(filters = 128, kernel_size = (5,5),padding = 'same',activation ='relu',
input_shape = (64,64,1))(input_layer)
encoded_layer = MaxPool2D(pool_size=(2,2))(encoded_layer)
encoded_layer = Dropout(0.25)(encoded_layer)
encoded_layer = (Conv2D(filters = 64, kernel_size = (3,3),padding = 'same',activation ='relu'))(encoded_layer)
encoded_layer = (MaxPool2D(pool_size=(2,2)))(encoded_layer)
encoded_layer = (Dropout(0.25))(encoded_layer)
encoded_layer = (Conv2D(filters = 64, kernel_size = (3,3),padding = 'same',activation ='relu'))(encoded_layer)
encoded_layer = (MaxPool2D(pool_size=(2,2)))(encoded_layer)
encoded_layer = (Dropout(0.25))(encoded_layer)
encoded_layer = Conv2D(filters = 1, kernel_size = (3,3),padding = 'same',activation ='relu',
input_shape = (64,64,1),strides=1)(encoded_layer)
encoded_layer = ZeroPadding2D(padding=(28, 28), data_format=None)(encoded_layer)
decoded_imag = Conv2D(8, (2, 2), activation='relu', padding='same')(encoded_layer)
decoded_imag = UpSampling2D((2, 2))(decoded_imag)
decoded_imag = Conv2D(8, (3, 3), activation='relu', padding='same')(decoded_imag)
decoded_imag = UpSampling2D((2, 2))(decoded_imag)
decoded_imag = Conv2D(16, (3, 3), activation='relu', padding='same')(decoded_imag)
decoded_imag = UpSampling2D((2, 2))(decoded_imag)
decoded_imag = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(decoded_imag)
flat_layer = Flatten()(decoded_imag)
dense_layer = Dense(256,activation = "relu")(flat_layer)
dense_layer = Dense(64,activation = "relu")(dense_layer)
dense_layer = Dense(32,activation = "relu")(dense_layer)
output_layer = Dense(9, activation = "softmax")(dense_layer)
autoencoder = Model(input_layer, [encoded_layer,output_layer])
autoencoder.summary()
autoencoder.compile(optimizer='adadelta', loss=[custom_loss1,custom_loss2])
autoencoder.fit(x_train,[x_train, y_train],batch_size=32,epochs=3,shuffle=True,
validation_data=(x_val, [x_val,y_val]))
数据维度：
x_train.shape: (4000, 64, 64, 1)
x_val.shape: (1000, 64, 64, 1)
y_train.shape: (4000, 9)
y_val.shape: (1000, 9)
损失看起来像：
def custom_loss1(y_true,y_pred):
dcor = -1*distance_correlation(y_true,encoded_layer)
return dcor
def custom_loss2(y_true,y_pred):
recon_loss = losses.categorical_crossentropy(y_true, y_pred)
return recon_loss
相关函数基于张量如下：
def distance_correlation(y_true,y_pred):
pred_r = tf.reduce_sum(y_pred*y_pred,1)
pred_r = tf.reshape(pred_r,[-1,1])
pred_d = pred_r - 2*tf.matmul(y_pred,tf.transpose(y_pred))+tf.transpose(pred_r)
true_r = tf.reduce_sum(y_true*y_true,1)
true_r = tf.reshape(true_r,[-1,1])
true_d = true_r - 2*tf.matmul(y_true,tf.transpose(y_true))+tf.transpose(true_r)
concord = 1-tf.matmul(y_true,tf.transpose(y_true))
#print(pred_d)
#print(tf.reshape(tf.reduce_mean(pred_d,1),[-1,1]))
#print(tf.reshape(tf.reduce_mean(pred_d,0),[1,-1]))
#print(tf.reduce_mean(pred_d))
tf.check_numerics(pred_d,'pred_d has NaN')
tf.check_numerics(true_d,'true_d has NaN')
A = pred_d - tf.reshape(tf.reduce_mean(pred_d,1),[-1,1]) - tf.reshape(tf.reduce_mean(pred_d,0),[1,-1]) + tf.reduce_mean(pred_d)
B = true_d - tf.reshape(tf.reduce_mean(true_d,1),[-1,1]) - tf.reshape(tf.reduce_mean(true_d,0),[1,-1]) + tf.reduce_mean(true_d)
#dcor = -tf.reduce_sum(concord*pred_d)/tf.reduce_sum((1-concord)*pred_d)
dcor = -tf.log(tf.reduce_mean(A*B))+tf.log(tf.sqrt(tf.reduce_mean(A*A)*tf.reduce_mean(B*B)))#-tf.reduce_sum(concord*pred_d)/tf.reduce_sum((1-concord)*pred_d)
#print(dcor.shape)
#tf.Print(dcor,[dcor])
#dcor = tf.tile([dcor],batch_size)
return (dcor)
模型摘要如下：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) (None, 64, 64, 1) 0
_________________________________________________________________
conv2d_30 (Conv2D) (None, 64, 64, 128) 3328
_________________________________________________________________
max_pooling2d_13 (MaxPooling (None, 32, 32, 128) 0
_________________________________________________________________
dropout_13 (Dropout) (None, 32, 32, 128) 0
_________________________________________________________________
conv2d_31 (Conv2D) (None, 32, 32, 64) 73792
_________________________________________________________________
max_pooling2d_14 (MaxPooling (None, 16, 16, 64) 0
_________________________________________________________________
dropout_14 (Dropout) (None, 16, 16, 64) 0
_________________________________________________________________
conv2d_32 (Conv2D) (None, 16, 16, 64) 36928
_________________________________________________________________
max_pooling2d_15 (MaxPooling (None, 8, 8, 64) 0
_________________________________________________________________
dropout_15 (Dropout) (None, 8, 8, 64) 0
_________________________________________________________________
conv2d_33 (Conv2D) (None, 8, 8, 1) 577
_________________________________________________________________
zero_padding2d_5 (ZeroPaddin (None, 64, 64, 1) 0
_________________________________________________________________
conv2d_34 (Conv2D) (None, 64, 64, 8) 40
_________________________________________________________________
up_sampling2d_10 (UpSampling (None, 128, 128, 8) 0
_________________________________________________________________
conv2d_35 (Conv2D) (None, 128, 128, 8) 584
_________________________________________________________________
up_sampling2d_11 (UpSampling (None, 256, 256, 8) 0
_________________________________________________________________
conv2d_36 (Conv2D) (None, 256, 256, 16) 1168
_________________________________________________________________
up_sampling2d_12 (UpSampling (None, 512, 512, 16) 0
_________________________________________________________________
conv2d_37 (Conv2D) (None, 512, 512, 1) 145
_________________________________________________________________
flatten_4 (Flatten) (None, 262144) 0
_________________________________________________________________
dense_13 (Dense) (None, 256) 67109120
_________________________________________________________________
dense_14 (Dense) (None, 64) 16448
_________________________________________________________________
dense_15 (Dense) (None, 32) 2080
_________________________________________________________________
dense_16 (Dense) (None, 9) 297
=================================================================
Total params: 67,244,507
Trainable params: 67,244,507
Non-trainable params: 0
_________________________________________________________________
这是错误：
InvalidArgumentError Traceback (most recent call last)
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1658 try:
-> 1659 c_op = c_api.TF_FinishOperation(op_desc)
1660 except errors.InvalidArgumentError as e:
InvalidArgumentError: Dimensions must be equal, but are 1 and 64 for 'loss_1/zero_padding2d_5_loss/MatMul' (op: 'BatchMatMul') with input shapes: [?,64,64,1], [1,64,64,?].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-11-0e924885fc6b> in <module>
40 autoencoder = Model(input_layer, [encoded_layer,output_layer])
41 autoencoder.summary()
---> 42 autoencoder.compile(optimizer='adadelta', loss=[custom_loss1,custom_loss2])
43 autoencoder.fit(x_train,[x_train, y_train],batch_size=32,epochs=3,shuffle=True,
44 validation_data=(x_val, [x_val,y_val]))
~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, **kwargs)
340 with K.name_scope(self.output_names[i] + '_loss'):
341 output_loss = weighted_loss(y_true, y_pred,
--> 342 sample_weight, mask)
343 if len(self.outputs) > 1:
344 self.metrics_tensors.append(output_loss)
~/anaconda3/lib/python3.6/site-packages/keras/engine/training_utils.py in weighted(y_true, y_pred, weights, mask)
402 """
403 # score_array has ndim >= 2
--> 404 score_array = fn(y_true, y_pred)
405 if mask is not None:
406 # Cast the mask to floatX to avoid float64 upcasting in Theano
<ipython-input-11-0e924885fc6b> in custom_loss1(y_true, y_pred)
2 #Wrappers for keras
3 def custom_loss1(y_true,y_pred):
----> 4 dcor = -1*distance_correlation(y_true,encoded_layer)
5 return dcor
6
<ipython-input-6-f282528532cc> in distance_correlation(y_true, y_pred)
2 pred_r = tf.reduce_sum(y_pred*y_pred,1)
3 pred_r = tf.reshape(pred_r,[-1,1])
----> 4 pred_d = pred_r - 2*tf.matmul(y_pred,tf.transpose(y_pred))+tf.transpose(pred_r)
5 true_r = tf.reduce_sum(y_true*y_true,1)
6 true_r = tf.reshape(true_r,[-1,1])
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py in matmul(a, b, transpose_a, transpose_b, adjoint_a, adjoint_b, a_is_sparse, b_is_sparse, name)
2415 adjoint_b = True
2416 return gen_math_ops.batch_mat_mul(
-> 2417 a, b, adj_x=adjoint_a, adj_y=adjoint_b, name=name)
2418
2419 # Neither matmul nor sparse_matmul support adjoint, so we conjugate
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py in batch_mat_mul(x, y, adj_x, adj_y, name)
1421 adj_y = _execute.make_bool(adj_y, "adj_y")
1422 _, _, _op = _op_def_lib._apply_op_helper(
-> 1423 "BatchMatMul", x=x, y=y, adj_x=adj_x, adj_y=adj_y, name=name)
1424 _result = _op.outputs[:]
1425 _inputs_flat = _op.inputs
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
786 op = g.create_op(op_type_name, inputs, output_types, name=scope,
787 input_types=input_types, attrs=attr_protos,
--> 788 op_def=op_def)
789 return output_structure, op_def.is_stateful, op
790
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs)
505 'in a future version' if date is None else ('after %s' % date),
506 instructions)
--> 507 return func(*args, **kwargs)
508
509 doc = _add_deprecated_arg_notice_to_docstring(
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in create_op(***failed resolving arguments***)
3298 input_types=input_types,
3299 original_op=self._default_original_op,
-> 3300 op_def=op_def)
3301 self._create_op_helper(ret, compute_device=compute_device)
3302 return ret
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1821 op_def, inputs, node_def.attr)
1822 self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
-> 1823 control_input_ops)
1824
1825 # Initialize self._outputs.
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1660 except errors.InvalidArgumentError as e:
1661 # Convert to ValueError for backwards compatibility.
-> 1662 raise ValueError(str(e))
1663
1664 return c_op
ValueError: Dimensions must be equal, but are 1 and 64 for 'loss_1/zero_padding2d_5_loss/MatMul' (op: 'BatchMatMul') with input shapes: [?,64,64,1], [1,64,64,?].
您有两个损失函数，因此您必须传递两个 y（基本事实）来评估与预测相关的损失。
您的第一个预测是 encoded_layer 层的输出，从 conv2d_59 (Conv2D)
(None, 8, 8, 128)
但是您为 y 传递的是 [x_train, y_train]。 loss_1 期望输入大小为 (None, 8, 8, 128) 但您传递的 x_train 具有不同的大小。
如果您希望 loss_1 找到输入图像与编码图像的相关性，然后堆叠卷积，这样卷积的输出将产生与您的形状相同的形状 x_train 图像形状。使用 model.summary 查看卷积的输出形状。
不使用卷积层的填充、步幅和内核大小来获得所需的卷积输出大小。使用公式 W2=(W1−F+2P)/S+1 和 H2=(H1−F+2P)/S+1 求出卷积的输出宽度和高度。检查这个 reference
您的方法有两个主要问题。
- 你的损失函数正在检查编码图像和实际图像之间的相关性。正确的做法是从编码图像解码回图像，然后检查解码图像和实际图像之间的相关性（在自动编码器的行中）
- 你的损失 1 使用了 numpy 数组。对于要成为计算图的一部分的损失函数，它应该使用张量运算，而不是 numy 运算。
下面是工作代码。但是，对于损失 1，我使用了两个图像的 l2 范数。如果你想使用相关性，那么你必须以某种方式将它转换为张量运算（这与这个问题不同）
def image_loss(y_true,y_pred):
return tf.norm(y_true - y_pred)
def label_loss(y_true,y_pred):
return categorical_crossentropy(y_true, y_pred)
input_img = Input(shape=(64, 64, 1))
enocded_imag = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
enocded_imag = MaxPooling2D((2, 2), padding='same')(enocded_imag)
enocded_imag = Conv2D(8, (3, 3), activation='relu', padding='same')(enocded_imag)
enocded_imag = MaxPooling2D((2, 2), padding='same')(enocded_imag)
enocded_imag = Conv2D(8, (3, 3), activation='relu', padding='same')(enocded_imag)
enocded_imag = MaxPooling2D((2, 2), padding='same')(enocded_imag)
decoded_imag = Conv2D(8, (2, 2), activation='relu', padding='same')(enocded_imag)
decoded_imag = UpSampling2D((2, 2))(decoded_imag)
decoded_imag = Conv2D(8, (3, 3), activation='relu', padding='same')(decoded_imag)
decoded_imag = UpSampling2D((2, 2))(decoded_imag)
decoded_imag = Conv2D(16, (3, 3), activation='relu', padding='same')(decoded_imag)
decoded_imag = UpSampling2D((2, 2))(decoded_imag)
decoded_imag = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(decoded_imag)
flat_layer = Flatten()(enocded_imag)
dense_layer = Dense(32,activation = "relu")(flat_layer)
output_layer = Dense(9, activation = "softmax")(dense_layer)
model = Model(input_img, [decoded_imag, output_layer])
model.compile(optimizer='adadelta', loss=[image_loss, label_loss])
images = np.random.randn(10,64,64,1)
model.fit(images, [images, np.random.randn(10,9)])
您编写的损失函数 distance_correlation 假定 y_true 和 y_pred 中的每一行代表一个图像。当您使用 Dense 层时，它会起作用，因为 Dense 层输出一批（行）向量，其中每个向量代表一个单独的图像。但是，二维卷积对具有多个通道的一批二维张量进行操作（您只有一个通道）。因此，要使用 distance_correlation 损失函数，您必须重塑张量，使每一行对应一个图像。添加以下两行以重塑您的张量。
def distance_correlation(y_true,y_pred):
y_true = tf.reshape(tf.squeeze(y_true), [-1,64*64])
y_pred = tf.reshape(tf.squeeze(y_pred), [-1,64*64])
.... REST OF THE CODE ....
目的是使用custom_loss1中的原始图像和custom_loss2中的标量标签值。我认为@mujjiga 在他的回答中的工作代码几乎是正确的。我建议稍微修改一下。
在model.compile()中传递需要它的损失中的输入张量。保持另一个不变。 model.fit() 只是传递标签。
model.compile(optimizer='adadelta', loss=[custom_loss1(input_layer), custom_loss2])
mode.fit(x_train, y_train)
自定义损失函数内部：
def custom_loss1(input):
def loss1(y_true, y_pred):
return tf.norm(input - y_pred) # use your custom loss 1
return loss1
def custom_loss2(y_true, y_pred):
return categorical_crossentropy(y_true, y_pred) # use your custom loss 2
先用简单的 in-built Keras 损失函数试试这个。如果效果不错，请查看您的自定义损失函数。