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pythontensorflownetwork-programmingdeep-learning
Stefano Kira
Stefano Kira

Reputation: 175

Invalid argument error (incompatible shapes) with TensorFlow

I'm trying to train a simple network with tensorflow for the MNIST dataset. At the moment though it is not working. It is basically a modified version of the example given on the TensorFlow website. I just changed a couple lines and removed a layer to see what happened. Here is my code:

#!/usr/bin/python

import input_data
import tensorflow as tf
#MNIST dataset
def weight_variable(shape):
    initial=tf.truncated_normal(shape,stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial=tf.constant(0.1,shape=shape)
    return tf.Variable(initial)
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)
x=tf.placeholder("float",shape=[None,784])
y=tf.placeholder("float",shape=[None,10])
x_image=tf.reshape(x,[-1,28,28,1])

#Layer 1: convolutional+max pooling

W_conv2=weight_variable([5,5,1,64])
b_conv2=bias_variable([64])

h_conv2=tf.nn.relu(conv2d(x_image,W_conv2)+b_conv2)
h_pool2=max_pool_2x2(h_conv2)

#Layer 2: ReLU+Dropout

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)

keep_prob=tf.placeholder("float")
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)

#Layer 3: softmax

W_fc4=weight_variable([1024,10])
b_fc4=bias_variable([10])
y_hat=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc4)+b_fc4)

cross_entropy=-tf.reduce_sum(y*tf.log(y_hat))
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
sess=tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_hat,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))

for n in range(20000):
    batch=mnist.train.next_batch(50)
    if n % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict={x:batch[0],y:batch[1],keep_prob:1.0})
        print "step %d,training accuracy %g" % (n,train_accuracy)
    sess.run(train_step,feed_dict={x:batch[0],y:batch[1],keep_prob:0.5})

print "test accuracy %g" % accuracy.eval(feed_dict={x:mnist.test.images, y:mnist.test.labels,keep_prob:1.0})

When I try to execute it it crashes giving me an ArgumentError:

W tensorflow/core/common_runtime/executor.cc:1027] 0x7fceb58a4200 Compute status: Invalid argument: Incompatible shapes: [50] vs. [200]

Upvotes: 3

Views: 16799

Answers (1)

Tristan Reid
Tristan Reid

Reputation: 6154

You need for your stride size to reduce your outputs to the right shape - this should fix it (note the strides compared to yours):

def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 2, 2, 1], padding='SAME')

To troubleshoot this kind of issue, try printing .get_shape() for all of your variables. Both Tensor and Variable have this function - it will give you a better sense of what's going on and will help immensely with troubleshooting.

Here's some code that will help - put this after your declaration of h_pool2, it will print the name and shape of each of your vars:

from tensorflow.python.ops.variables import Variable

for k, v in locals().items():
    if type(v) is Variable or type(v) is tf.Tensor:
        print("{0}: {1}".format(k, v))

Upvotes: 3

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