model.add(LSTM(HIDDEN_LAYER_SIZE, dropout=0.2, recurrent_dropout=0.2))
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/models.py", line 475, in add
output_tensor = layer(self.outputs[0])
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/layers/recurrent.py", line 268, in __call__
return super(Recurrent, self).__call__(inputs, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/topology.py", line 575, in __call__
self.build(input_shapes[0])
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/layers/recurrent.py", line 1050, in build
constraint=self.bias_constraint)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 87, in wrapper
return func(*args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/topology.py", line 396, in add_weight
weight = K.variable(initializer(shape),
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/layers/recurrent.py", line 1042, in bias_initializer
self.bias_initializer((self.units * 2,), *args, **kwargs),
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 1709, in concatenate
return tf.concat([to_dense(x) for x in tensors],axis)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 1075, in concat
Returns:
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 669, in convert_to_tensor
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-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 "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py", line 367, in make_tensor_proto
_AssertCompatible(values, dtype)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py", line 302, in _AssertCompatible
(dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected int32, got list containing Tensors of type '_Message' instead.
python3 -c 'import keras; print(keras.__version__)'
I only take tensorflow_backend.py in concatenate(tensors, axis)
Change this:
return tf.concat([to_dense(x) for x in tensors], axis)
To this:
return tf.concat(axis,[to_dense(x) for x in tensors])
