SnakeCharmer - 1 year ago 316

Python Question

I'm trying to implement an asynchronous parameter server, DistBelief style using TensorFlow. I found that minimize() is split into two functions, compute_gradients and apply_gradients, so my plan is to insert a network boundary between them. I have a question about how to evaluate all the gradients simultaneously and pull them out all at once. I understand that eval only evaluates the subgraph necessary, but it also only returns one tensor, not the chain of tensors required to compute that tensor.

How can I do this more efficiently? I took the Deep MNIST example as a starting point:

`import tensorflow as tf`

import download_mnist

def weight_variable(shape, name):

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

return tf.Variable(initial, name=name)

def bias_variable(shape, name):

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

return tf.Variable(initial, name=name)

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 = download_mnist.read_data_sets('MNIST_data', one_hot=True)

session = tf.InteractiveSession()

x = tf.placeholder("float", shape=[None, 784], name='x')

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

y_ = tf.placeholder("float", shape=[None, 10], name='y_')

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

b_conv1 = bias_variable([32], 'b_conv1')

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

h_pool1 = max_pool_2x2(h_conv1)

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

b_conv2 = bias_variable([64], 'b_conv2')

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

h_pool2 = max_pool_2x2(h_conv2)

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

b_fc1 = bias_variable([1024], 'b_fc1')

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)

keep_prob = tf.placeholder("float", name='keep_prob')

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

W_fc2 = weight_variable([1024, 10], 'W_fc2')

b_fc2 = bias_variable([10], 'b_fc2')

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

loss = -tf.reduce_sum(y_ * tf.log(y_conv))

optimizer = tf.train.AdamOptimizer(1e-4)

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

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

compute_gradients = optimizer.compute_gradients(loss)

session.run(tf.initialize_all_variables())

batch = mnist.train.next_batch(50)

feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}

gradients = []

for grad_var in compute_gradients:

grad = grad_var[0].eval(feed_dict=feed_dict)

var = grad_var[1]

gradients.append((grad, var))

I think this last for loop is actually recalculating the last gradient several times, whereas the first gradient is computed only once? How can I grab all the gradients without recomputing them?

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Answer Source

Just give you a simple example. Understand it and try your specific task out.

Initialize required symbols.

```
x = tf.Variable(0.5)
y = x*x
opt = tf.train.AdagradOptimizer(0.1)
grads = opt.compute_gradients(y)
grad_placeholder = [(tf.placeholder("float", shape=grad[1].get_shape()), grad[1] for grad in grads]
apply_placeholder_op = opt.apply_gradients(grad_placeholder)
transform_grads = [(function1(grad[0]), grad[1]) for grad in grads]
apply_transform_op = opt.apply_gradients(transform_grads)
```

Initialize

```
sess = tf.Session()
sess.run(tf.initialize_all_variables())
```

Get all gradients

```
grad_vals = sess.run([grad[0] for grad in grads])
```

Apply gradients

```
feed_dict = {}
for i in xrange(len(grad_placeholder)):
feed_dict[grad_placeholder[i][0]] = function2(grad_vals[i])
sess.run(apply_placeholder_op, feed_dict=feed_dict)
sess.run(apply_transform_op)
```

Note: the code hasn't been tested by myself, but I confirm the code is legal except minor code errors. Note: function1 and function2 is kind of computation, such as 2*x, x^e or e^x and so on.

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