Martin Thoma - 1 year ago 267

Python Question

I would like to be able to access the restored weights of a model.

I know how to get the weights directly after training:

`def weight_variable(shape):`

initial = tf.truncated_normal(shape, stddev=0.1)

return tf.Variable(initial, name='weights')

with tf.variable_scope('conv1') as scope:

W_conv1 = weight_variable([5, 5, 1, 32])

[train]

weights_ = W_conv1.eval()

print(weights_)

However, the following does not work:

`saver.restore(sess, model_checkpoint_path)`

with tf.variable_scope('conv1') as scope_conv:

W_conv1 = tf.get_variable('weights', shape=[5, 5, 1, 32])

weights_ = W_conv1.eval()

print(weights_)

It gives

`Model restored.`

Traceback (most recent call last):

File "./tf_mnist.py", line 122, in <module>

weights_ = W_conv1.eval()

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 445, in eval

return self._variable.eval(session=session)

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 559, in eval

return _eval_using_default_session(self, feed_dict, self.graph, session)

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 3761, in _eval_using_default_session

return session.run(tensors, feed_dict)

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 717, in run

run_metadata_ptr)

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 915, in _run

feed_dict_string, options, run_metadata)

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 965, in _do_run

target_list, options, run_metadata)

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 985, in _do_call

raise type(e)(node_def, op, message)

tensorflow.python.framework.errors.FailedPreconditionError: Attempting to use uninitialized value conv1/weights_1

[[Node: conv1/weights_1/_52 = _Send[T=DT_FLOAT, client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_4_conv1/weights_1", _device="/job:localhost/replica:0/task:0/gpu:0"](conv1/weights_1)]]

[[Node: conv1/weights_1/_53 = _Recv[_start_time=0, client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_4_conv1/weights_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

How can I access the restored model weights?

Execute the following twice to see what I mean. The first time, the model will be trained and saved to disk (takes about 5 minutes after the data is downloaded). The second run restores the model.

`#!/usr/bin/env python`

"""MNIST with Tensorflow."""

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

import os

epochs = 1000

model_checkpoint_path = 'checkpoints/mnist_tf_model.ckpt'

def weight_variable(shape):

initial = tf.truncated_normal(shape, stddev=0.1)

return tf.Variable(initial, name='weights')

def bias_variable(shape):

initial = tf.constant(0.1, shape=shape)

return tf.Variable(initial, name='biases')

def conv2d(x, W):

return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):

return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],

strides=[1, 2, 2, 1], padding='SAME')

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

with tf.Session() as sess:

x = tf.placeholder(tf.float32, shape=[None, 784])

y_ = tf.placeholder(tf.float32, shape=[None, 10])

x_image = tf.reshape(x, [-1, 28, 28, 1])

with tf.variable_scope('conv1') as scope:

W_conv1 = weight_variable([5, 5, 1, 32])

b_conv1 = bias_variable([32])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1, name='ReLU1')

h_pool1 = max_pool_2x2(h_conv1)

with tf.variable_scope('conv2') as scope:

W_conv2 = weight_variable([5, 5, 32, 64])

b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2, name='ReLU2')

h_pool2 = max_pool_2x2(h_conv2)

with tf.variable_scope('fc1'):

W_fc1 = weight_variable([7 * 7 * 64, 1024])

b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])

h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

with tf.variable_scope('dropout'):

keep_prob = tf.placeholder(tf.float32)

h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

with tf.variable_scope('softmax'):

W_fc2 = weight_variable([1024, 10])

b_fc2 = bias_variable([10])

y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv),

reduction_indices=[1]))

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# Add ops to save and restore all the variables.

saver = tf.train.Saver()

sess.run(tf.initialize_all_variables())

if not os.path.isfile(model_checkpoint_path):

for i in range(epochs):

batch = mnist.train.next_batch(50)

train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

# Save the variables to disk.

save_path = saver.save(sess, model_checkpoint_path)

print("Model saved in file: %s" % save_path)

weights_ = W_conv1.eval()

print(weights_)

else:

saver.restore(sess, model_checkpoint_path)

print("Model restored.")

summary_writer = tf.train.SummaryWriter('summary_dir', sess.graph)

with tf.variable_scope('conv1') as scope_conv:

W_conv1 = tf.get_variable('weights', shape=[5, 5, 1, 32])

weights_ = W_conv1.eval()

print(weights_)

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Answer Source

You should not mix `tf.get_variable`

and `tf.Variable`

.

You should define variables at the beginning as:

```
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.get_variable('weights', initializer=initial)
```

and then read them like this:

```
with tf.variable_scope('conv1', reuse=True) as scope_conv:
W_conv1 = tf.get_variable('weights', shape=[5, 5, 1, 32])
weights_ = W_conv1.eval()
print(weights_)
```

note the `reuse=True`

argument.

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