【发布时间】：2017-08-27 14:06:03
【问题描述】：
这是我的代码，当我运行它时，我无法理解错误，为什么u'rnn_cell_40/Tanh:0 需要提要值？
import numpy as np
import tensorflow as tf
import reader
raw_data = reader.ptb_raw_data('simple-examples/data')
train_data, val_data, test_data, num_classes = raw_data
batch_size = 200
num_steps = 40
def build_graph(num_steps,
bptt_steps = 20, batch_size = 200, num_classes = num_classes,
state_size = 4, embed_size = 50, learning_rate = 0.01):
g = tf.get_default_graph()
x = tf.placeholder(tf.int32, [batch_size, None], name='input_placeholder')
y = tf.placeholder(tf.int32, [batch_size, None], name='labels_placeholder')
default_init_state = tf.zeros([batch_size, state_size])
init_state = tf.placeholder_with_default(default_init_state,
[batch_size, state_size], name='state_placeholder')
dropout = tf.placeholder(tf.float32, [], name='dropout_placeholder')
x_one_hot = tf.one_hot(x, num_classes)
x_as_list = [tf.squeeze(i, squeeze_dims=[1]) for i in tf.split(x_one_hot, num_steps, 1)]
with tf.variable_scope('embeddings'):
embeddings = tf.get_variable('embedding_matrix', [num_classes, embed_size])
def embedding_lookup(one_hot_input):
with tf.variable_scope('embeddings', reuse=True):
embeddings = tf.get_variable('embedding_matrix', [num_classes, embed_size])
embeddings = tf.identity(embeddings)
g.add_to_collection('embeddings', embeddings)
return tf.matmul(one_hot_input, embeddings)
rnn_inputs = [embedding_lookup(i) for i in x_as_list]
rnn_inputs = [tf.nn.dropout(x, dropout) for x in rnn_inputs]
# rnn_cells
with tf.variable_scope('rnn_cell'):
W = tf.get_variable('W', [embed_size + state_size, state_size])
b = tf.get_variable('b', [state_size], initializer=tf.constant_initializer(0.0))
def rnn_cell(rnn_input, state):
with tf.variable_scope('rnn_cell', reuse=True):
W = tf.get_variable('W', [embed_size + state_size, state_size])
W = tf.identity(W)
g.add_to_collection('Ws', W)
b = tf.get_variable('b', [state_size], initializer=tf.constant_initializer(0.0))
b = tf.identity(b)
g.add_to_collection('bs', b)
return tf.tanh(tf.matmul(tf.concat([rnn_input, state], 1), W) + b)
state = init_state
rnn_outputs = []
for rnn_input in rnn_inputs:
state = rnn_cell(rnn_input, state)
print state.name
rnn_outputs.append(state)
print init_state.name
#apply dropout to outputs
rnn_outputs = [tf.nn.dropout(x, dropout) for x in rnn_outputs]
final_state = rnn_outputs[-1]
print final_state.name
#logits and predictions
with tf.variable_scope('softmax'):
W = tf.get_variable('W_softmax', [state_size, num_classes])
b = tf.get_variable('b_softmax', [num_classes], initializer=tf.constant_initializer(0.0))
logits = [tf.matmul(rnn_output, W) + b for rnn_output in rnn_outputs]
predictions = [tf.nn.softmax(logit) for logit in logits]
#losses
y_as_list = [tf.squeeze(i, squeeze_dims=[1]) for i in tf.split(y, num_steps, 1)]
losses = [tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logit,labels=label) \
for logit, label in zip(logits, y_as_list)]
total_loss = tf.reduce_mean(losses)
"""
Implementation of true truncated backprop using TF's high-level gradients function.
Because I add gradient-ops for each error, this are a number of duplicate operations,
making this a slow implementation. It would be considerably more effort to write an
efficient implementation, however, so for testing purposes, it's OK that this goes slow.
An efficient implementation would still require all of the same operations as the full
backpropagation through time of errors in a sequence, and so any advantage would not come
from speed, but from having a better distribution of backpropagated errors.
"""
embed_by_step = g.get_collection('embeddings')
Ws_by_step = g.get_collection('Ws')
bs_by_step = g.get_collection('bs')
# Collect gradients for each step in a list
embed_grads = []
W_grads = []
b_grads = []
# Keeping track of vanishing gradients for my own curiousity
vanishing_grad_list = []
# Loop through the errors, and backpropagate them to the relevant nodes
for i in range(num_steps):
start = max(0,i+1-bptt_steps)
stop = i+1
grad_list = tf.gradients(losses[i],
embed_by_step[start:stop] +\
Ws_by_step[start:stop] +\
bs_by_step[start:stop])
embed_grads += grad_list[0 : stop - start]
W_grads += grad_list[stop - start : 2 * (stop - start)]
b_grads += grad_list[2 * (stop - start) : ]
if i >= bptt_steps:
vanishing_grad_list.append(grad_list[stop - start : 2 * (stop - start)])
grad_embed = tf.add_n(embed_grads) / (batch_size * bptt_steps)
grad_W = tf.add_n(W_grads) / (batch_size * bptt_steps)
grad_b = tf.add_n(b_grads) / (batch_size * bptt_steps)
opt = tf.train.AdamOptimizer(learning_rate)
grads_and_vars_tf_style = opt.compute_gradients(total_loss, tf.trainable_variables())
grads_and_vars_true_bptt = \
[(grad_embed, tf.trainable_variables()[0]),
(grad_W, tf.trainable_variables()[1]),
(grad_b, tf.trainable_variables()[2])] + \
opt.compute_gradients(total_loss, tf.trainable_variables()[3:])
train_tf_style = opt.apply_gradients(grads_and_vars_tf_style)
train_true_bptt = opt.apply_gradients(grads_and_vars_true_bptt)
return dict(
train_tf_style = train_tf_style,
train_true_bptt = train_true_bptt,
gvs_tf_style = grads_and_vars_tf_style,
gvs_true_bptt = grads_and_vars_true_bptt,
gvs_gradient_check = opt.compute_gradients(losses[-1], tf.trainable_variables()),
loss = total_loss,
final_state = final_state,
x=x,
y=y,
init_state=init_state,
dropout=dropout,
vanishing_grads=vanishing_grad_list
)
def reset_graph():
if 'sess' in globals() and sess:
sess.close()
tf.reset_default_graph()
reset_graph()
g = build_graph(num_steps = 40, bptt_steps = 20)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
X, Y = next(reader.ptb_iterator(train_data, batch_size=200, num_steps=40))
gvs_bptt = sess.run(g['gvs_true_bptt'], feed_dict={g['x']:X, g['y']:Y, g['dropout']: 1})
这是结果：
ValueError Traceback (most recent call last)
<ipython-input-1-a5d0b8513bfb> in <module>()
205 # gvs_bptt = sess.run(g['gvs_true_bptt'], feed_dict={g['x']:X, g['y']:Y, g['dropout']: 1})
206
--> 207 gvs_bptt = sess.run(g['gvs_true_bptt'], feed_dict={g['x']:X, g['y']:Y, g['dropout']: 1})
/root/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
776 try:
777 result = self._run(None, fetches, feed_dict, options_ptr,
--> 778 run_metadata_ptr)
779 if run_metadata:
780 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/root/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
959 'Cannot feed value of shape %r for Tensor %r, '
960 'which has shape %r'
--> 961 % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
962 if not self.graph.is_feedable(subfeed_t):
963 raise ValueError('Tensor %s may not be fed.' % subfeed_t)
ValueError: Cannot feed value of shape (200, 40) for Tensor u'rnn_cell_40/Tanh:0', which has shape '(200, 4)'
【问题讨论】：
标签： python machine-learning tensorflow deep-learning