Error of the matrix matrix of the tensor matrix

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, 1)
   resized.set_shape([180,180,3])
   flipped_images = tf.image.flip_up_down(resized)
   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')



image,label = inputs()
W_conv1=weight_variable([5,5,3,32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)


W_conv2=weights_variable([5,5,32,64])
b_conv2 = bias_variable([32])
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 * 32, 512])
b_fc1 = bias_variable([512])

h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*32])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
W_fc2 = weight_variable([512, 10])
b_fc2 = bias_variable([10])

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

cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))   
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
tf.train.start_queue_runners(sess=sess)
for i in xrange(100):
    img,label = sess.run(image)
    print (label)
    train_step.run({img, label, 0.5})

When I run the code, I get an error,

"ValueError: ShapesTensorShape([Dimension(180),Dimension(180),Dimension(3)]) and TensorShape([Dimension(None), Dimension(None), Dimension(None), Dimension(None)]) must have the same rank"

But the scales were initialized, and even then they show them as empty tensors. Files and tags are read and transferred correctly. The first convolutional layer has a 5x5 window with a depth of 3 and I want to have 32 such 5X5 filters. Therefore, the form [5,5,3,32] for W_conv1.

Answer 1

The function inputs()returns a form tensor 180 x 180 x 3, but tf.nn.conv2d()expects a four-dimensional form tensor batch_size x height x width x num_channels.

etarion, , image (, image = tf.expand_dims(image, 0)). , , , , . - tf.train.batch():

image, label = inputs()

# Set batch_size to the largest value that works for your configuration.
image_batch, label_batch = tf.train.batch([image, label], batch_size=32)

... image_batch label_batch, image label .

Answer 2

, , conv2d 4d ( - idx) - , [1,180,180,3].