问题
I am having problems making effective usage of variable scopes. I want to define some variables for weights, biases and inner state of a simple recurrent network. I call get_saver() once after defining the default graph. I then iterate over a batch of samples using tf.scan.
import tensorflow as tf
import math
import numpy as np
INPUTS = 10
HIDDEN_1 = 2
BATCH_SIZE = 3
def batch_vm2(m, x):
[input_size, output_size] = m.get_shape().as_list()
input_shape = tf.shape(x)
batch_rank = input_shape.get_shape()[0].value - 1
batch_shape = input_shape[:batch_rank]
output_shape = tf.concat(0, [batch_shape, [output_size]])
x = tf.reshape(x, [-1, input_size])
y = tf.matmul(x, m)
y = tf.reshape(y, output_shape)
return y
def get_saver():
with tf.variable_scope('h1') as scope:
weights = tf.get_variable('W', shape=[INPUTS, HIDDEN_1], initializer=tf.truncated_normal_initializer(stddev=1.0 / math.sqrt(float(INPUTS))))
biases = tf.get_variable('bias', shape=[HIDDEN_1], initializer=tf.constant_initializer(0.0))
state = tf.get_variable('state', shape=[HIDDEN_1], initializer=tf.constant_initializer(0.0), trainable=False)
saver = tf.train.Saver([weights, biases, state])
return saver
def load(sess, saver, checkpoint_dir = None):
print("loading a session")
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise Exception("no checkpoint found")
return
def iterate_state(prev_state_tuple, input):
with tf.variable_scope('h1') as scope:
scope.reuse_variables()
weights = tf.get_variable('W', shape=[INPUTS, HIDDEN_1], initializer=tf.truncated_normal_initializer(stddev=1.0 / math.sqrt(float(INPUTS))))
biases = tf.get_variable('bias', shape=[HIDDEN_1], initializer=tf.constant_initializer(0.0))
state = tf.get_variable('state', shape=[HIDDEN_1], initializer=tf.constant_initializer(0.0), trainable=False)
print("input: ",input.get_shape())
matmuladd = batch_vm2(weights, input) + biases
matmulpri = tf.Print(matmuladd,[matmuladd], message=" malmul -> ")
#matmulvec = tf.reshape(matmuladd, [HIDDEN_1])
#state = tf.get_variable('state', shape=[HIDDEN_1], initializer=tf.constant_initializer(0.0))
print("prev state: ",prev_state_tuple.get_shape())
unpacked_state, unpacked_out = tf.split(0,2,prev_state_tuple)
prev_state = unpacked_state
state = state.assign( 4.2*(0.9* prev_state + 0.1*matmuladd) )
#output = tf.nn.relu(state)
output = tf.nn.tanh(state)
state = tf.Print(state, [state], message=" state -> ")
output = tf.Print(output, [output], message=" output -> ")
#output = matmulpri
print(" state: ", state.get_shape())
print(" output: ", output.get_shape())
concat_result = tf.concat(0,[state, output])
print (" concat return: ", concat_result.get_shape())
return concat_result
def data_iter():
while True:
idxs = np.random.rand(BATCH_SIZE, INPUTS)
yield idxs
with tf.Graph().as_default():
inputs = tf.placeholder(tf.float32, shape=(BATCH_SIZE, INPUTS))
saver = get_saver()
initial_state = tf.zeros([HIDDEN_1],
name='initial_state')
initial_out = tf.zeros([HIDDEN_1],
name='initial_out')
#concat_tensor = tf.concat(0,[initial_state, initial_out])
concat_tensor = tf.concat(0,[initial_state, initial_out])
print(" init state: ",initial_state.get_shape())
print(" init out: ",initial_out.get_shape())
print(" concat: ",concat_tensor.get_shape())
scanout = tf.scan(iterate_state, inputs, initializer=concat_tensor, name='state_scan')
print ("scanout shape: ", scanout.get_shape())
state, output = tf.split(1,2,scanout, name='split_scan_output')
print(" end state: ",state.get_shape())
print(" end out: ",output.get_shape())
#output,state,diagnostic = create_graph(inputs, state, prev_state)
sess = tf.Session()
# Run the Op to initialize the variables.
sess.run(tf.initialize_all_variables())
if False:
load(sess, saver)
iter_ = data_iter()
for i in xrange(0, 5):
print ("iteration: ",i)
input_data = iter_.next()
out,st,so = sess.run([output,state,scanout], feed_dict={ inputs: input_data})
saver.save(sess, 'my-model', global_step=1+i)
print("input vec: ", input_data)
print("state vec: ", st)
print("output vec: ", out)
print(" end state (runtime): ",st.shape)
print(" end out (runtime): ",out.shape)
print(" end scanout (runtime): ",so.shape)
My hope would be to have the variables retrieved from get_variable inside the scan op to be the same as defined inside the get_saver call. However if I run this sample code I get the following output with errors:
(' init state: ', TensorShape([Dimension(2)]))
(' init out: ', TensorShape([Dimension(2)]))
(' concat: ', TensorShape([Dimension(4)]))
Traceback (most recent call last):
File "cycles_in_graphs_with_scan.py", line 88, in <module>
scanout = tf.scan(iterate_state, inputs, initializer=concat_tensor, name='state_scan')
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/functional_ops.py", line 345, in scan
back_prop=back_prop, swap_memory=swap_memory)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 1873, in while_loop
result = context.BuildLoop(cond, body, loop_vars)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 1749, in BuildLoop
body_result = body(*vars_for_body_with_tensor_arrays)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/functional_ops.py", line 339, in compute
a = fn(a, elems_ta.read(i))
File "cycles_in_graphs_with_scan.py", line 47, in iterate_state
weights = tf.get_variable('W', shape=[INPUTS, HIDDEN_1], initializer=tf.truncated_normal_initializer(stddev=1.0 / math.sqrt(float(INPUTS))))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 732, in get_variable
partitioner=partitioner, validate_shape=validate_shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 596, in get_variable
partitioner=partitioner, validate_shape=validate_shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 161, in get_variable
caching_device=caching_device, validate_shape=validate_shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 454, in _get_single_variable
" Did you mean to set reuse=None in VarScope?" % name)
ValueError: Variable state_scan/h1/W does not exist, disallowed. Did you mean to set reuse=None in VarScope?
any idea what I am doing wrong in this example?
回答1:
if False:
load(sess, saver)
This two lines lead to uninitialized variables.
来源：https://stackoverflow.com/questions/37680219/variable-scopes-in-tensorflow