Mixing
ValueError: Shape must be rank 2 but is rank 3
I'm trying to run my code for a RNN, but got this error:
ValueError: Shape must be rank 2 but is rank 3 for 'echo_state_rnn_cell/MatMul_3' (op: 'MatMul') with input shapes: [2,1,1000], [1000,1000].
The thing that I don't understand is why my tensor has three dimensions, it should be [1,1000].
My code is:
esn_init_state = np.zeros([1, res_size], dtype="float32")
tf.reset_default_graph()
static_graph = tf.Graph()
with static_graph.as_default() as g:
rng = np.random.RandomState(random_seed)
cell = EchoStateRNNCell(num_inputs=input_size, num_units=res_size, decay=0.1, leaky=leak,
epsilon=1e-10, alpha=0.0100, rng=rng)
X = tf.placeholder(tf.float32, [input_size+res_size, trainlen-initlen])
Yt = data[None, initlen+1:trainlen+1]
init_state = tf.placeholder(tf.float32, [1, res_size])
inputs = tf.placeholder(tf.float32, [trainlen,input_size]) #modifica postuma
state = init_state
for t in range(trainlen):
prev_state = state
state = cell(inputs=inputs[t:t+1,:], state=prev_state)
if t >= initlen:
X[:,t-initlen] = np.vstack((inputs,state))[:,0]
The error should be in: state = cell(inputs=inputs[t:t+1,:], state=prev_state).
Here is the definition of cell function:
def call(self, inputs, state):
new_state = (1 - self._leaky*self.decay) * state + self.decay*self._activation(math_ops.matmul(inputs,self.W) + math_ops.matmul(self._activation(state), self.U*self.rho_one))
output = self._activation(new_state)
return output, new_state
Thanks for your help!
python tensorflow
edited Nov 22 '18 at 11:14
Anubhav Singh
1431212
asked Nov 22 '18 at 9:19
nabbonabbo
12
add a comment |
I'm trying to run my code for a RNN, but got this error:
ValueError: Shape must be rank 2 but is rank 3 for 'echo_state_rnn_cell/MatMul_3' (op: 'MatMul') with input shapes: [2,1,1000], [1000,1000].
The thing that I don't understand is why my tensor has three dimensions, it should be [1,1000].
My code is:
esn_init_state = np.zeros([1, res_size], dtype="float32")
tf.reset_default_graph()
static_graph = tf.Graph()
with static_graph.as_default() as g:
rng = np.random.RandomState(random_seed)
cell = EchoStateRNNCell(num_inputs=input_size, num_units=res_size, decay=0.1, leaky=leak,
epsilon=1e-10, alpha=0.0100, rng=rng)
X = tf.placeholder(tf.float32, [input_size+res_size, trainlen-initlen])
Yt = data[None, initlen+1:trainlen+1]
init_state = tf.placeholder(tf.float32, [1, res_size])
inputs = tf.placeholder(tf.float32, [trainlen,input_size]) #modifica postuma
state = init_state
for t in range(trainlen):
prev_state = state
state = cell(inputs=inputs[t:t+1,:], state=prev_state)
if t >= initlen:
X[:,t-initlen] = np.vstack((inputs,state))[:,0]
The error should be in: state = cell(inputs=inputs[t:t+1,:], state=prev_state).
Here is the definition of cell function:
def call(self, inputs, state):
new_state = (1 - self._leaky*self.decay) * state + self.decay*self._activation(math_ops.matmul(inputs,self.W) + math_ops.matmul(self._activation(state), self.U*self.rho_one))
output = self._activation(new_state)
return output, new_state
Thanks for your help!
python tensorflow
edited Nov 22 '18 at 11:14
Anubhav Singh
1431212
asked Nov 22 '18 at 9:19
nabbonabbo
12
add a comment |
I'm trying to run my code for a RNN, but got this error:
ValueError: Shape must be rank 2 but is rank 3 for 'echo_state_rnn_cell/MatMul_3' (op: 'MatMul') with input shapes: [2,1,1000], [1000,1000].
The thing that I don't understand is why my tensor has three dimensions, it should be [1,1000].
My code is:
esn_init_state = np.zeros([1, res_size], dtype="float32")
tf.reset_default_graph()
static_graph = tf.Graph()
with static_graph.as_default() as g:
rng = np.random.RandomState(random_seed)
cell = EchoStateRNNCell(num_inputs=input_size, num_units=res_size, decay=0.1, leaky=leak,
epsilon=1e-10, alpha=0.0100, rng=rng)
X = tf.placeholder(tf.float32, [input_size+res_size, trainlen-initlen])
Yt = data[None, initlen+1:trainlen+1]
init_state = tf.placeholder(tf.float32, [1, res_size])
inputs = tf.placeholder(tf.float32, [trainlen,input_size]) #modifica postuma
state = init_state
for t in range(trainlen):
prev_state = state
state = cell(inputs=inputs[t:t+1,:], state=prev_state)
if t >= initlen:
X[:,t-initlen] = np.vstack((inputs,state))[:,0]
The error should be in: state = cell(inputs=inputs[t:t+1,:], state=prev_state).
Here is the definition of cell function:
def call(self, inputs, state):
new_state = (1 - self._leaky*self.decay) * state + self.decay*self._activation(math_ops.matmul(inputs,self.W) + math_ops.matmul(self._activation(state), self.U*self.rho_one))
output = self._activation(new_state)
return output, new_state
Thanks for your help!
python tensorflow
edited Nov 22 '18 at 11:14
Anubhav Singh
1431212
asked Nov 22 '18 at 9:19
nabbonabbo
12
I'm trying to run my code for a RNN, but got this error:
ValueError: Shape must be rank 2 but is rank 3 for 'echo_state_rnn_cell/MatMul_3' (op: 'MatMul') with input shapes: [2,1,1000], [1000,1000].
The thing that I don't understand is why my tensor has three dimensions, it should be [1,1000].
My code is:
esn_init_state = np.zeros([1, res_size], dtype="float32")
tf.reset_default_graph()
static_graph = tf.Graph()
with static_graph.as_default() as g:
rng = np.random.RandomState(random_seed)
cell = EchoStateRNNCell(num_inputs=input_size, num_units=res_size, decay=0.1, leaky=leak,
epsilon=1e-10, alpha=0.0100, rng=rng)
X = tf.placeholder(tf.float32, [input_size+res_size, trainlen-initlen])
Yt = data[None, initlen+1:trainlen+1]
init_state = tf.placeholder(tf.float32, [1, res_size])
inputs = tf.placeholder(tf.float32, [trainlen,input_size]) #modifica postuma
state = init_state
for t in range(trainlen):
prev_state = state
state = cell(inputs=inputs[t:t+1,:], state=prev_state)
if t >= initlen:
X[:,t-initlen] = np.vstack((inputs,state))[:,0]
The error should be in: state = cell(inputs=inputs[t:t+1,:], state=prev_state).
Here is the definition of cell function:
def call(self, inputs, state):
new_state = (1 - self._leaky*self.decay) * state + self.decay*self._activation(math_ops.matmul(inputs,self.W) + math_ops.matmul(self._activation(state), self.U*self.rho_one))
output = self._activation(new_state)
return output, new_state
Thanks for your help!
python tensorflow
python tensorflow
edited Nov 22 '18 at 11:14
Anubhav Singh
1431212
asked Nov 22 '18 at 9:19
nabbonabbo
12
edited Nov 22 '18 at 11:14
Anubhav Singh
1431212
asked Nov 22 '18 at 9:19
nabbonabbo
12
edited Nov 22 '18 at 11:14
Anubhav Singh
1431212
edited Nov 22 '18 at 11:14
Anubhav Singh
1431212
edited Nov 22 '18 at 11:14
Anubhav Singh
1431212
1431212
asked Nov 22 '18 at 9:19
nabbonabbo
12
asked Nov 22 '18 at 9:19
nabbonabbo
12
asked Nov 22 '18 at 9:19
nabbonabbo
12
12
add a comment |
add a comment |
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