Tensorflow error “shape Tensorshape() must have rank 1”

Question

import tensorflow as tf
import numpy as np
import os
from PIL import Image
cur_dir = os.getcwd()

def modify_image(image):
  #resized = tf.image.resize_images(image, 180, 180, 3)
   image.set_shape([32,32,3])
   flipped_images = tf.image.flip_up_down(image)
   return flipped_images

def read_image(filename_queue):
  reader = tf.WholeFileReader()
  key,value = reader.read(filename_queue)
  image = tf.image.decode_jpeg(value)
  return key,image

def inputs():
 filenames = ['standard_1.jpg', 'standard_2.jpg' ]
 filename_queue = tf.train.string_input_producer(filenames)
 filename,read_input = read_image(filename_queue)
 reshaped_image = modify_image(read_input)
 reshaped_image = tf.cast(reshaped_image, tf.float32)
 label=tf.constant([1])
 return reshaped_image,label

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')

x = tf.placeholder(tf.float32, shape=[None,32,32,3])
y_ = tf.placeholder(tf.float32, shape=[None, 1])
image,label=inputs()
image=tf.reshape(image,[-1,32,32,3])
label=tf.reshape(label,[-1,1])
image_batch=tf.train.batch([image],batch_size=2)
label_batch=tf.train.batch([label],batch_size=2)

W_conv1 = weight_variable([5, 5, 3, 32])
b_conv1 = bias_variable([32])

image_4d=x_image = tf.reshape(image, [-1,32,32,3])

h_conv1 = tf.nn.relu(conv2d(image_4d, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

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

W_fc1 = weight_variable([8 * 8 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 2])
b_fc2 = bias_variable([2])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy= -tf.reduce_sum(tf.cast(image_batch[1],tf.float32)*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

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

for i in range(20000):
 sess.run(train_step,feed_dict={x:image_batch[0:1],y_:label_batch[0:1]})

I am trying to run the tensorflow convolutional model over my own images of dimension of[32x32x3] images. The images are being read properly and are being assigned to the placeholder, during training. The problem arises during running the train_step op. When I execute the graph I get the following error.

TensorShape([Dimension(2), Dimension(1), Dimension(32), Dimension(32), Dimension(3)]) must have rank 1

But when I see the example here , the images are in the form of [batch_size,height,width,depth] tensors only. And this example works fine. Am I missing anything?

Answer 1

I think the error is coming from this line:

cross_entropy= -tf.reduce_sum(tf.cast(image_batch[1],tf.float32)*tf.log(y_conv))

image_batch is a 5-D tensor, with shape [2, 1, 32, 32, 3] , where 2 is the batch_size argument to tf.train.batch() , and the 1 was added by the earlier image = tf.reshape(image, [-1, 32, 32, 3]) . ( NB This reshape is unnecessary, since tf.train.batch() already adds a batch dimension, and you end up having to undo the effect of the reshape when you later construct image_4d ).

In TensorFlow, the slicing operation (ie image_batch[1] ) is slightly less flexible than in NumPy. The number of dimensions specified in the slice must be equal to the rank of the tensor: ie you must specify all five dimensions for this to work. You could specify image_batch[1, :, :, :, :] to get a 4-D slice of image_batch .

I noticed a few other issues in your program, however:

1. The cross_entropy calculation seems strange. Typically this uses a predicted label and compares it to the known label , as opposed to the image data.

2. The feed on the training step seems to have no effect, because placeholders x and y_ are unused in your program. Furthermore, you appear to be feeding a tf.Tensor (indeed, an illegal slice of image_batch ), so this will fail when you execute that statement. If you intend to use feeding, you should feed in NumPy arrays holding the input data.

3. If you're not using feeding—ie using the tf.WholeFileReader as shown in your program—you will need to call tf.train.start_queue_runners() to get started. Otherwise your program will hang, waiting for input.