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I am learning TensorFlow and I stumble upon this example code for creating simple multi-layer sigmoid network. The program in the link is for MNIST database and hand written digit classification.

I want to train a network for regression task. I have 30 inputs(float) which is used to predict one output(float). So I tweaked the code to change the task from classification to regression.

My problem is that I'm getting an error in tf.Session.run(). The code and the error log is given below.

import test2

import tensorflow as tf



feed_input = test2.read_data_sets()



learning_rate = 0.001

training_epochs = 100

batch_size = 1716

display_step = 1



n_hidden_1 = 256

n_hidden_2 = 256

n_hidden_3 = 256

n_input = 30



x = tf.placeholder("float", [None, n_input])

y = tf.placeholder("float", [None])



def multilayer_perceptron(_X, _weights, _biases):

    #Hidden layer with RELU activation

    layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) 

    #Hidden layer with RELU activationn_hidden_3

    layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) 

    layer_3 = tf.nn.relu(tf.add(tf.matmul(layer_2, _weights['h3']), _biases['b3']))     



    return tf.matmul(layer_3, weights['out']) + biases['out']



weights = {

    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),

    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),

    'h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3])),

    'out': tf.Variable(tf.random_normal([n_hidden_3, 1]))

}

biases = {

    'b1': tf.Variable(tf.random_normal([n_hidden_1])),

    'b2': tf.Variable(tf.random_normal([n_hidden_2])),

    'b3': tf.Variable(tf.random_normal([n_hidden_3])),

    'out': tf.Variable(tf.random_normal([1]))

}



pred = multilayer_perceptron(x, weights, biases)



n_pred = tf.mul(pred, tf.convert_to_tensor(10000.00))



cost = tf.nn.sigmoid_cross_entropy_with_logits(n_pred, y)



optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)



init = tf.initialize_all_variables()



with tf.Session() as sess:

    sess.run(init)



    # Training cycle

    for epoch in range(training_epochs):

        avg_cost = 0

        total_batch = int(feed_input.train._num_examples / batch_size)

        # Loop over all batches

        for i in range(total_batch):

            batch_xs, batch_ys = feed_input.train.next_batch(batch_size)

            # Fit training using batch data

            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})

            # Compute average loss

            avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys}) / total_batch

        # Display logs per epoch step

        if epoch % display_step == 0:

            print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)



    print "Optimization Finished!"

runfile('/mnt/sdb6/Projects/StockML/demo1.py',

wdir='/mnt/sdb6/Projects/StockML')

Reloaded modules: tensorflow.python.ops.nn_grad,

tensorflow.python.training.momentum,

. . . .

tensorflow.python.util.protobuf,

google.protobuf.internal.enum_type_wrapper,

tensorflow.python.ops.nn_ops, tensorflow.python,

tensorflow.python.platform.test,

google.protobuf.internal.api_implementation, tensorflow,

google.protobuf.internal.encoder

Traceback (most recent call last):

File "", line 1, in
runfile('/mnt/sdb6/Projects/StockML/demo1.py', wdir='/mnt/sdb6/Projects/StockML')

File
"/usr/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/sitecustomize.py",
line 685, in runfile
execfile(filename, namespace)

File
"/usr/lib/python2.7/dist-packages/spyderlib/widgets/externalshell/sitecustomize.py",
line 78, in execfile
builtins.execfile(filename, *where)

File "/mnt/sdb6/Projects/StockML/demo1.py", line 69, in
sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})

File
"/home/rammak/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py",
line 345, in run
results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)

File
"/home/rammak/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py",
line 406, in _do_run
except tf_session.StatusNotOK as e:

AttributeError: 'module' object has no attribute 'StatusNotOK'

Protobuf error is usually an installation issue , run it in a virtual env

# On Mac:

$ sudo easy_install pip  # If pip is not already installed

$ sudo pip install --upgrade virtualenv

Next, set up a new virtualenv environment. To set it up in the directory ~/tensorflow, run:

$ virtualenv --system-site-packages ~/tensorflow

$ cd ~/tensorflow

Then activate the virtualenv:

$ source bin/activate  # If using bash

$ source bin/activate.csh  # If using csh

(tensorflow)$  # Your prompt should change

Inside the virtualenv, install TensorFlow:

(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl

You can then run your TensorFlow program like:

(tensorflow)$ python tensorflow/models/image/mnist/convolutional.py



# When you are done using TensorFlow:

(tensorflow)$ deactivate  # Deactivate the virtualenv



$  # Your prompt should change back

If you just begin to learn TensorFlow, I would suggest you trying out examples in TensorFlow/skflow first and then once you are more familiar with TensorFlow it would be fairly easy for you to insert TensorFlow code to build a custom model you want (there are also examples for this).

Hope those examples for images and text understanding could get you started and let us know if you encounter any issues! (post issues or tag skflow in SO).

Change your logging level from WARN to INFO, so that can get a better visualization of the error you're getting.

For knowledge purpose, you should know there are 5 logging levels:

1. 
   DEBUG


2. 
   INFO


3. 
   WARN


4. 
   ERROR


5. 
   FATAL