The line: LSTM (1000,activation = 'tanh',return_sequences = True)(ht) produces a strange error for me:
/usr/local/lib/python3.5/dist-packages/keras/layers/recurrent.py in get_initial_states(self, x)
202 def get_initial_states(self, x):
203 # build an all-zero tensor of shape (samples, output_dim)
--> 204 initial_state = K.zeros_like(x) # (samples, timesteps, input_dim)
205 initial_state = K.sum(initial_state, axis=(1, 2)) # (samples,)
206 initial_state = K.expand_dims(initial_state) # (samples, 1)
/usr/local/lib/python3.5/dist-packages/keras/backend/mxnet_backend.py in zeros_like(x, name)
822 if name is None:
823 name = _autogen_name('zerolikeinit')
--> 824 y = mx._symbol_internal._zeros(dtype=dtype(x))
825 return KerasSymbol(mx._symbol_internal._identity_with_attr_like_rhs(y, x.symbol), name=name, is_var=True)
826
AttributeError: module 'mxnet' has no attribute '_symbol_internal'
mxnet version 0.12.0 Keras v1.2.2
The line: LSTM (1000,activation = 'tanh',return_sequences = True)(ht) produces a strange error for me:
/usr/local/lib/python3.5/dist-packages/keras/layers/recurrent.py in get_initial_states(self, x)
202 def get_initial_states(self, x):
203 # build an all-zero tensor of shape (samples, output_dim)
--> 204 initial_state = K.zeros_like(x) # (samples, timesteps, input_dim)
205 initial_state = K.sum(initial_state, axis=(1, 2)) # (samples,)
206 initial_state = K.expand_dims(initial_state) # (samples, 1)
/usr/local/lib/python3.5/dist-packages/keras/backend/mxnet_backend.py in zeros_like(x, name)
822 if name is None:
823 name = _autogen_name('zerolikeinit')
--> 824 y = mx._symbol_internal._zeros(dtype=dtype(x))
825 return KerasSymbol(mx._symbol_internal._identity_with_attr_like_rhs(y, x.symbol), name=name, is_var=True)
826
AttributeError: module 'mxnet' has no attribute '_symbol_internal'
mxnet version 0.12.0 Keras v1.2.2