我正在尝试使用Keras为文本序列构建字符级自动编码器。当我编译模型时，我会看到有关张量形状的错误，如下所示。我打印出要检查的图层规格，如果张量形状匹配，我发现问题可能是最后一个没有正确指定输出张量形状的Lambda图层，但我无法想象了解为什么没有或如何指定它，并且在Keras或Google的文档中没有找到任何相关内容。
错误输出下面也是代码的一部分，我定义了我的模型。如果需要，整个脚本澄清如下：PasteBin。
（主要是关注最后一层。）
0 <keras.engine.topology.InputLayer object at 0x7f5d290eb588> Input shape (None, 80) Output shape (None, 80)
1 <keras.layers.core.Lambda object at 0x7f5d35f25a20> Input shape (None, 80) Output shape (None, 80, 99)
2 <keras.layers.core.Dense object at 0x7f5d2dda52e8> Input shape (None, 80, 99) Output shape (None, 80, 256)
3 <keras.layers.core.Dropout object at 0x7f5d25004da0> Input shape (None, 80, 256) Output shape (None, 80, 256)
4 <keras.layers.core.Dense object at 0x7f5d2501ac18> Input shape (None, 80, 256) Output shape (None, 80, 128)
5 <keras.layers.core.Dense object at 0x7f5d24dc6cc0> Input shape (None, 80, 128) Output shape (None, 80, 64)
6 <keras.layers.core.Dense object at 0x7f5d24de1fd0> Input shape (None, 80, 64) Output shape (None, 80, 128)
7 <keras.layers.core.Dropout object at 0x7f5d24df4a20> Input shape (None, 80, 128) Output shape (None, 80, 128)
8 <keras.layers.core.Dense object at 0x7f5d24dfeb38> Input shape (None, 80, 128) Output shape (None, 80, 256)
9 <keras.layers.core.Lambda object at 0x7f5d24da6a20> Input shape (None, 80, 256) Output shape (None, 80)
----------------
0 Input Tensor("input_1:0", shape=(?, 80), dtype=int64) Output Tensor("input_1:0", shape=(?, 80), dtype=int64)
1 Input Tensor("input_1:0", shape=(?, 80), dtype=int64) Output Tensor("ToFloat:0", shape=(?, 80, 99), dtype=float32)
2 Input Tensor("ToFloat:0", shape=(?, 80, 99), dtype=float32) Output Tensor("Relu:0", shape=(?, 80, 256), dtype=float32)
3 Input Tensor("Relu:0", shape=(?, 80, 256), dtype=float32) Output Tensor("cond/Merge:0", shape=(?, 80, 256), dtype=float32)
4 Input Tensor("cond/Merge:0", shape=(?, 80, 256), dtype=float32) Output Tensor("Relu_1:0", shape=(?, 80, 128), dtype=float32)
5 Input Tensor("Relu_1:0", shape=(?, 80, 128), dtype=float32) Output Tensor("Relu_2:0", shape=(?, 80, 64), dtype=float32)
6 Input Tensor("Relu_2:0", shape=(?, 80, 64), dtype=float32) Output Tensor("Relu_3:0", shape=(?, 80, 128), dtype=float32)
7 Input Tensor("Relu_3:0", shape=(?, 80, 128), dtype=float32) Output Tensor("cond_1/Merge:0", shape=(?, 80, 128), dtype=float32)
8 Input Tensor("cond_1/Merge:0", shape=(?, 80, 128), dtype=float32) Output Tensor("truediv:0", shape=(?, 80, 256), dtype=float32)
9 Input Tensor("truediv:0", shape=(?, 80, 256), dtype=float32) Output Tensor("ToFloat_1:0", shape=(), dtype=float32)
----------------
Traceback (most recent call last):
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/tensor_shape.py", line 578, in merge_with
self.assert_same_rank(other)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/tensor_shape.py", line 624, in assert_same_rank
"Shapes %s and %s must have the same rank" % (self, other))
ValueError: Shapes (?, ?) and () must have the same rank
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/nn_impl.py", line 153, in sigmoid_cross_entropy_with_logits
labels.get_shape().merge_with(logits.get_shape())
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/tensor_shape.py", line 585, in merge_with
(self, other))
ValueError: Shapes (?, ?) and () are not compatible
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "lstm.py", line 97, in <module>
autoencoder.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
File "/usr/local/lib/python3.4/dist-packages/keras/engine/training.py", line 667, in compile
sample_weight, mask)
File "/usr/local/lib/python3.4/dist-packages/keras/engine/training.py", line 318, in weighted
score_array = fn(y_true, y_pred)
File "/usr/local/lib/python3.4/dist-packages/keras/objectives.py", line 45, in binary_crossentropy
return K.mean(K.binary_crossentropy(y_pred, y_true), axis=-1)
File "/usr/local/lib/python3.4/dist-packages/keras/backend/tensorflow_backend.py", line 2449, in binary_crossentropy
logits=output)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/nn_impl.py", line 156, in sigmoid_cross_entropy_with_logits
% (logits.get_shape(), labels.get_shape()))
ValueError: logits and labels must have the same shape (() vs (?, ?))
我使用此代码构建了我的模型：
def binarize(x, sz):
return tf.to_float(tf.one_hot(x, sz, on_value=1, off_value=0, axis=-1))
def binarize_outputshape(in_shape):
return in_shape[0], in_shape[1], len(chars)
def debinarize(x):
return tf.to_float(np.argmax(x)) # get the character with most probability
def debinarize_outputshape(in_shape):
return in_shape[0], in_shape[1]
input_sentence = Input(shape=(max_title_len,), dtype='int64')
# make one-hot vectors out of sentences
one_hot = Lambda(binarize, output_shape=binarize_outputshape, arguments={'sz': len(chars)})(input_sentence)
# shape: max_title_len * chars = 80 * 55 = 4400
encoder = Dense(256, activation='relu')(one_hot)
encoder = Dropout(0.1)(encoder)
encoder = Dense(128, activation='relu')(encoder)
encoder = Dense(64, activation='relu')(encoder)
decoder = Dense(128, activation='relu')(encoder)
encoder = Dropout(0.1)(encoder)
decoder = Dense(256, activation='softmax')(decoder)
# transform back from one-hot vectors
decoder = Lambda(debinarize, output_shape=debinarize_outputshape)(decoder)
autoencoder = Model(input=input_sentence, output=decoder)
首先，我输入最多80个字符的文本序列，Lambda层将每个字符转换为单热矢量。最后，我想将单热矢量转换回来，同时仅将最大值作为解码字符。
正如Nassim Ben指出的那样，问题在于 debinarize 。将其更改为：
def debinarize(x):
return tf.to_float(tf.argmax(x, axis=0))
至少某种值被设置为输出张量的形状。虽然值有点奇怪，因为它是（80,256）并且与输出形状（无，80）不同。所有其他输出张量形状和输出形状相应地匹配（我认为'？'和None意味着相同或多或少......）。更具体地说，Lambda层现在看起来像这样：
<keras.layers.core.Lambda object at 0x7fafcc5a59b0> Input shape (None, 80, 256) Output shape (None, 80)
...
...
Input Tensor("truediv:0", shape=(?, 80, 256), dtype=float32) Output Tensor("ToFloat_1:0", shape=(80, 256), dtype=float32)
问题是，我想输出张量形状为（？，80），就像第一层的输入一样。我没有更改任何其他代码而不是 argmax 。
现在给出的错误是：
Traceback (most recent call last):
File "lstm.py", line 122, in <module>
callbacks=[earlystop_cb, check_cb, keras.callbacks.TensorBoard(log_dir='/tmp/autoencoder')])
File "/usr/local/lib/python3.4/dist-packages/keras/engine/training.py", line 1168, in fit
self._make_train_function()
File "/usr/local/lib/python3.4/dist-packages/keras/engine/training.py", line 760, in _make_train_function
self.total_loss)
File "/usr/local/lib/python3.4/dist-packages/keras/optimizers.py", line 433, in get_updates
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/math_ops.py", line 883, in binary_op_wrapper
y = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name="y")
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/ops.py", line 651, in convert_to_tensor
as_ref=False)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/ops.py", line 716, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/constant_op.py", line 165, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/tensor_util.py", line 360, in make_tensor_proto
raise ValueError("None values not supported.")
ValueError: None values not supported.
答案 0 :(得分：2)
我认为它来自于在张量上使用numpy函数。 尝试使用tf argmax函数（我想你要减少的轴是1，不确定）
def debinarize(x):
return tf.to_float(tf.argmax(x,axis=2)) # get the character with most probability
这有用吗？