turn this into pytorch don't use alternative just pure translate : def weight_variable(shape, name=""):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial, name)

def bias_variable(shape, name=""):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial, name)

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

def max_pool_2x2(x, name=""):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME', name=name)

x = tf.placeholder(tf.float32, [None, 32*32])

# First layer : 32 features
W_conv1 = weight_variable([5, 5, 1, 32], name='W1')
b_conv1 = bias_variable([32], name='B1')

x_image = tf.reshape(x, [-1,32,32,1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1, name='Conv1')
h_pool1 = max_pool_2x2(h_conv1, name='Pool1')

# Second convolutional layer : 64 features
W_conv2 = weight_variable([5, 5, 32, 64], name='W2')
b_conv2 = bias_variable([64], name='B2')

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2, name='Conv2')
h_pool2 = max_pool_2x2(h_conv2, name='Pool2')

# Densely connected layer : 1024 neurons, image size now 8x8
W_fc1 = weight_variable([8 * 8 * 64, 1024], name='W3')
b_fc1 = bias_variable([1024], name='B3')

h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64], name='Pool3')
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1, 'MatMult3')

# Dropout
keep_prob = tf.placeholder("float", name='KeepProb')
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob, name='Drop4')

# Readout layer : softmax, 13 features
W_fc2 = weight_variable([1024, 13], name='W5')
b_fc2 = bias_variable([13], name='B5')

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

# # Old single layer regression classifier
# W = tf.Variable(tf.zeros([32*32, 13]))
# b = tf.Variable(tf.zeros([13]))
# y = tf.nn.softmax(tf.matmul(x, W) + b)

# Ground truth labels if exist 
y_ = tf.placeholder(tf.float32, [None, 13], name='Ytruth')

cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv), name='CrossEntropy')

# train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1), name='CorrectPrediction')
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"), name='Accuracy')

# Add ops to save and restore all the variables.
saver = tf.train.Saver()

# Start Interactive session for rest of notebook (else we'd want to close session)
sess = tf.InteractiveSession()

# Training vs loading existing model
do_training = False

# Number of steps
N = 10000

if do_training:
    #Initialize session
    sess.run(tf.initialize_all_variables())
    
    # Training
    print "Training for %d steps..." % N
    for i in range(N):
        # Get next batch for training
        batch_xs, batch_ys = train_dataset.next_batch(100)

        # Print out progress to screen
        if ((i+1) % 100) == 0:
            train_accuracy = accuracy.eval(feed_dict={
                x:batch_xs, y_: batch_ys, keep_prob: 1.0})
            print "\n\t%d/%d, training accuracy %g" % (i+1, N, train_accuracy),
        elif ((i+1) % 10) == 0:
            print '.',

        # Train model with batch
        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})

    print "Finished training."

    # Save model checkpoint
    save_path = saver.save(sess, "saved_models/model_%d.ckpt" % N)
    print "Model saved in file: ", save_path

else:
    # Restore model from checkpoint
    model_name = "saved_models/model_%d.ckpt" % N
    print "Loading model '%s'" % model_name
    saver.restore(sess, model_name)
    print "Model restored."
    
# Testing
print "Accuracy: %g\n" % accuracy.eval(feed_dict={x: test_dataset.images,
                                                  y_: test_dataset.labels,
                                                  keep_prob: 1.0})