My Enviroment Windows10 Anaconda Tensorflow 1.1.0 Python 3.5
I classify images using a neural network. I use an image of 1024 * 768 pixels, but since the calculation amount is enormous when using all the pixels, I introduced a random mask to randomly sample 1000 pixels. However, this kind of error has occurred and it has not been successfully sampled successfully
”ValueError: Dimensions must be equal, but are 786432 and 1728 for 'fc1/MatMul' (op: 'MatMul') with input shapes: [?,786432], [1000,10].”
Why does it look like this? Could you tell me if there is a solution?
All codes are posted below.
import sys
sys.path.append('/usr/local/opt/opencv3/lib/python3.5.4/site-packages')
import cv2
import numpy as np
import tensorflow as tf
import tensorflow.python.platform
import tensorboard as tb
import os
import math
import time
import random
start_time = time.time()
# TensorBoard output information directory
log_dir = '/tmp/data1' #tensorboard --logdir=/tmp/data1
#directory delete and reconstruction
if tf.gfile.Exists(log_dir):
tf.gfile.DeleteRecursively(log_dir)
tf.gfile.MakeDirs(log_dir)
# Reserve memory
config = tf.ConfigProto(
gpu_options=tf.GPUOptions(
allow_growth=True
)
)
sess = sess = tf.Session(config=config)
NUM_CLASSES = 2
IMAGE_SIZE_x = 1024
IMAGE_SIZE_y = 768
IMAGE_CHANNELS = 1
IMAGE_PIXELS = IMAGE_SIZE_x*IMAGE_SIZE_y*IMAGE_CHANNELS
SAMPLE_PIXELS = 1000
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('train', 'train.txt', 'File name of train data')
flags.DEFINE_string('test', 'test.txt', 'File name of train data')
flags.DEFINE_string('image_dir', 'data', 'Directory of images')
flags.DEFINE_string('train_dir', '/tmp/data', 'Directory to put the training data.')
flags.DEFINE_integer('max_steps', 20000, 'Number of steps to run trainer.')
flags.DEFINE_integer('batch_size', 10, 'Batch size'
'Must divide evenly into the dataset sizes.')
flags.DEFINE_float('learning_rate', 1e-5, 'Initial learning rate.')
#
def inference(images_placeholder, keep_prob):
""" Function to create predictive model
argument:
images_placeholder: image placeholder
keep_prob: dropout rate placeholder
Return:
y_out:
"""
# Initialie with normal distribution with weight of 0.1
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
# Initialized with normal distribution with bias of 0.1
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
# Random mask
mask = np.array([False]*IMAGE_PIXELS)
inds = np.random.choice(np.arange(IMAGE_PIXELS),size = SAMPLE_PIXELS)
mask[inds] = True
# Sample input
x_image = tf.boolean_mask(images_placeholder,mask)
# input
with tf.name_scope('fc1') as scope:
W_fc1 = weight_variable([SAMPLE_PIXELS,10])
b_fc1 = bias_variable([10])
h_fc1 = tf.nn.relu(tf.matmul(x_image,W_fc1) + b_fc1)
# hidden
with tf.name_scope('fc2') as scope:
W_fc2 = weight_variable([10,10])
b_fc2 = bias_variable([10])
h_fc2 = tf.nn.relu(tf.matmul(h_fc1,W_fc2) + b_fc2)
# output
with tf.name_scope('fc3') as scope:
W_fc5 = weight_variable([10,2])
b_fc5 = bias_variable([2])
# softmax regression
with tf.name_scope('softmax') as scope:
y_out=tf.nn.softmax(tf.matmul(h_fc4, W_fc5) + b_fc5)
# return
return y_out
def loss(logits, labels):
""" loss function
argument:
logits: logit tensor, float - [batch_size, NUM_CLASSES]
labels: labrl tensor, int32 - [batch_size, NUM_CLASSES]
return value:
cross_entropy:tensor, float
"""
# cross entropy
cross_entropy = -tf.reduce_sum(labels*tf.log(tf.clip_by_value(logits,1e-10,1.0)))
# TensorBoard
tf.summary.scalar("cross_entropy", cross_entropy)
return cross_entropy
def training(loss, learning_rate):
#Adam
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
return train_step
def accuracy(logits, labels):
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1)) #original
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))#original
tf.summary.scalar("accuracy", accuracy)#original
return accuracy#original
if __name__ == '__main__':
f = open(FLAGS.train, 'r')
# array data
train_image = []
train_label = []
for line in f:
# Separate space and remove newlines
line = line.rstrip()
l = line.split()
# Load data and resize
img = cv2.imread(FLAGS.image_dir + '/' + l[0])
img = cv2.resize(img, (IMAGE_SIZE_x, IMAGE_SIZE_y))
#transrate grayscale
img_gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# transrate one row and 0-1 float
train_image.append(img_gry.flatten().astype(np.float32)/255.0)
# Prepare with label 1-of-k method
tmp = np.zeros(NUM_CLASSES)
tmp[int(l[1])] = 1
train_label.append(tmp)
# transrate numpy
train_image = np.asarray(train_image)
train_label = np.asarray(train_label)
f.close()
f = open(FLAGS.test, 'r')
test_image = []
test_label = []
for line in f:
line = line.rstrip()
l = line.split()
img = cv2.imread(FLAGS.image_dir + '/' + l[0])
img = cv2.resize(img, (IMAGE_SIZE_x, IMAGE_SIZE_y))
#transrate grayscale
img_gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# transrate one row and 0-1 float
test_image.append(img_gry.flatten().astype(np.float32)/255.0)
tmp = np.zeros(NUM_CLASSES)
tmp[int(l[1])] = 1
test_label.append(tmp)
test_image = np.asarray(test_image)
test_label = np.asarray(test_label)
f.close()
with tf.Graph().as_default():
# Put the image Tensor
images_placeholder = tf.placeholder("float", shape=(None, IMAGE_PIXELS))
# Put the label Tensor
labels_placeholder = tf.placeholder("float", shape=(None, NUM_CLASSES))
# Put dropout rate Tensor
keep_prob = tf.placeholder("float")
# Load inference() and make model
logits = inference(images_placeholder, keep_prob)
# Load loss() and calculate loss
loss_value = loss(logits, labels_placeholder)
# Load training() and train
train_op = training(loss_value, FLAGS.learning_rate)
# calculate accuracy
acc = accuracy(logits, labels_placeholder)
# save
saver = tf.train.Saver()
# Make Session
sess = tf.Session()
# Initialize variable
sess.run(tf.global_variables_initializer())
# TensorBoard
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)
# Start training
for step in range(FLAGS.max_steps):
for i in range(int(len(train_image)/FLAGS.batch_size)):
batch = FLAGS.batch_size*i
sess.run(train_op, feed_dict={
images_placeholder: train_image[batch:batch+FLAGS.batch_size],
labels_placeholder: train_label[batch:batch+FLAGS.batch_size],
keep_prob: 0.5})
# Accuracy calculation for every steps
train_accuracy = sess.run(acc, feed_dict={
images_placeholder: train_image,
labels_placeholder: train_label,
keep_prob: 1.0})
print("step %d, training accuracy %g" %(step, train_accuracy))
# Added value to be displayed in Tensorflow every 1step
summary_str = sess.run(summary_op, feed_dict={
images_placeholder: train_image,
labels_placeholder: train_label,
keep_prob: 1.0})
summary_writer.add_summary(summary_str, step)
# Display accuracy on test data after training
print(" test accuracy %g"%sess.run(acc, feed_dict={
images_placeholder: test_image,
labels_placeholder: test_label,
keep_prob: 1.0}))
duration = time.time() - start_time
print('%.3f sec' %duration)
# Save model
save_path = saver.save(sess, os.getcwd() + "\\model.ckpt")
