CODEWITHSUNDEEP
pythonkerastensor
Fixining_ranges
Fixining_ranges

Reputation: 223

Iterate a tensor in a for loop?

How do i iterate a tensor in a for loop?..

I want to do convolution on each row of my input_tensor... but can't seem to iterate in a tensor.

Currently trying to it like this: 

def row_convolution(input):
    filter_size = 8
    print input.dtype
    print input.get_shape()
    for units in xrange(splits):
        extract = input[units:units+filter_size,:,:]
        for row_of_extract in extract:
            for unit in row_of_extract:
                temp_list.append((Conv1D(filters = 1, kernel_size = 1, activation='relu' , name = 'conv')(unit)))
            print len(temp_list)
            sum_temp_list.append(sum(temp_list))
        sum_sum_temp_list.append(sum(sum_temp_list))
    conv_feature_map.append(sum_sum_temp_list)
    return np.array(conv_feature_map)

Upvotes: 0

Views: 1675

Answers (1)

David Parks
David Parks

Reputation: 32071

It looks like you're trying to define tensorflow operations for each input. This is a common misunderstanding about the framework.

You must first define the operations that you will perform, all operations must be defined up front. Usually it looks something like this:

g = tf.Graph()
with g.as_default():
   # define some placeholders to accept your input
   X = tf.placeholder(tf.float32, shape=[1000,1])
   y = tf.placeholder(tf.float32, shape=[1])
   # add more operations...
   Conv1D(...)  # add your convolution operations
   # add the rest of your operations
   optimizer = tf.train.AdamOptimizer(0.00001).minimize(loss)

Now the graph has been defined, all of it. Consider that fixed, you won't add anything to it again.

Now you'll run data through the fixed graph:

with g.as_default(), tf.Session() as sess:
   X_data, y_data = get_my_data()
   # run this in a loop
   result = sess.run([optimizer,loss], feed_dict={X:X_data, y:y_data})

Note that your data and labels should be feed in a batch, so the first dimension of your data represents N number of datapoints (N=1 is perfectly acceptable of course). You should preprocess the data so it's in that format. For example, a batch of 10 MNIST digits would be in shape [10,28,28,1]. That's:

  • 10 data samples
  • Images are 28 px height
  • Images are 28 px width
  • It's a grayscale image, so 1 color channel

Upvotes: 1

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