من برنامه مشابه لینک زیر را
https://www.tensorflow.org/get_started/mnist/pros
برای تصاویر خودم به صورت زیر نوشتم: کد کامل
import numpy as np
import matplotlib.pyplot as plt
try:
from scipy import misc
except ImportError:
!pip install scipy
from scipy import misc
import tensorflow as tf
import numpy as np
import imageio
import matplotlib.pyplot as plt
try:
from scipy import misc
except ImportError:
!pip install scipy
from scipy import misc
training_size = 265
col_row = 400
img_size = 400*400
training_data = np.empty(shape=(training_size, 400*400))
import glob
i = 0
for filename in glob.glob('C:/Mah/TensorPalms/Train/*.jpg'):
image = imageio.imread(filename)
print(image.shape)
training_data[i] = image.reshape(-1)
i+=1
#label
a= [0,0,0,0,0,
1,1,1,1,1,
2,2,2,2,2,
3,3,3,3,3,
4,4,4,4,4,
5,5,5,5,5,
6,6,6,6,6,
7,7,7,7,7,
8,8,8,8,8,
9,9,9,9,9,
10,10,10,10,10,
11,11,11,11,11,
12,12,12,12,12,
13,13,13,13,13,
14,14,14,14,14,
15,15,15,15,15,
16,16,16,16,16,
17,17,17,17,17,
18,18,18,18,18,
19,19,19,19,19,
20,20,20,20,20,
21,21,21,21,21,
22,22,22,22,22,
23,23,23,23,23,
24,24,24,24,24,
25,25,25,25,25,
26,26,26,26,26,
27,27,27,27,27,
28,28,28,28,28,
29,29,29,29,29,
30,30,30,30,30,
31,31,31,31,31,
32,32,32,32,32,
33,33,33,33,33,
34,34,34,34,34,
35,35,35,35,35,
36,36,36,36,36,
37,37,37,37,37,
38,38,38,38,38,
39,39,39,39,39,
40,40,40,40,40,
41,41,41,41,41,
42,42,42,42,42,
43,43,43,43,43,
44,44,44,44,44,
45,45,45,45,45,
46,46,46,46,46,
47,47,47,47,47,
48,48,48,48,48,
49,49,49,49,49,
50,50,50,50,50,
51,51,51,51,51,
52,52,52,52,52,]
b = tf.one_hot(a,53)
sess = tf.Session()
sess.run(b)
print(b.shape)
from sklearn.preprocessing import OneHotEncoder
training_label = OneHotEncoder(sparse=False).fit_transform(np.asarray(a).reshape(-1, 1))
print(training_label)
#test
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
try:
from scipy import misc
except ImportError:
!pip install scipy
from scipy import misc
test_size = 159
img_size = 400*400
test_images = np.empty(shape=(test_size,400*400))
import glob
i = 0
for filename in glob.glob('C:/Mah/TensorPalms/Test/*.jpg'):
image = imageio.imread(filename)
print(image.shape)
test_images[i] = image.reshape(-1)
i+=1
c= [0,0,0,
1,1,1,
2,2,2,
3,3,3,
4,4,4,
5,5,5,
6,6,6,
7,7,7,
8,8,8,
9,9,9,
10,10,10,
11,11,11,
12,12,12,
13,13,13,
14,14,14,
15,15,15,
16,16,16,
17,17,17,
18,18,18,
19,19,19,
20,20,20,
21,21,21,
22,22,22,
23,23,23,
24,24,24,
25,25,25,
26,26,26,
27,27,27,
28,28,28,
29,29,29,
30,30,30,
31,31,31,
32,32,32,
33,33,33,
34,34,34,
35,35,35,
36,36,36,
37,37,37,
38,38,38,
39,39,39,
40,40,40,
41,41,41,
42,42,42,
43,43,43,
44,44,44,
45,45,45,
46,46,46,
47,47,47,
48,48,48,
49,49,49,
50,50,50,
51,51,51,
52,52,52]
test_labels = tf.one_hot(c,53)
sess = tf.Session()
sess.run(test_labels)
from sklearn.preprocessing import OneHotEncoder
test_label = OneHotEncoder(sparse=False).fit_transform(np.asarray(c).reshape(-1, 1))
print(test_label)
import tensorflow as tf
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, [None, img_size])
y_ = tf.placeholder(tf.float32, [None, 53])
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')
W_conv1 = weight_variable([5, 5, 3, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, 400, 400, 3])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
#Second Convolutional Layer
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)
#Densely Connected Layer
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)
#Dropout
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
#Readout Layer
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
import numpy as np
def next_batch(num, data, labels):
'''
Return a total of `num` random samples and labels.
'''
idx = np.arange(0 , len(data))
np.random.shuffle(idx)
idx = idx[:num]
data_shuffle = [data[ i] for i in idx]
labels_shuffle = [labels[ i] for i in idx]
return np.asarray(data_shuffle), np.asarray(labels_shuffle)
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=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))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch_xs, batch_ys = next_batch(50,training_data,training_label)
array_batch_xs = np.reshape(batch_xs, [-1, img_size ])
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: array_batch_xs, y_: batch_ys, keep_prob: 1.0})
print('step %d, training accuracy %g' % (i, train_accuracy))
train_step.run(feed_dict={x: array_batch_xs, y_: batch_ys, keep_prob: 0.5})
print('test accuracy %g' % accuracy.eval(feed_dict={
x: test_images, y_: test_label, keep_prob: 1.0}))
و خطای برنامه به صورت زیر است:
14 train_accuracy = accuracy.eval(feed_dict={
---> 15 x: array_batch_xs, y_: batch_ys, keep_prob: 1.0})
16 print('step %d, training accuracy %g' % (i, train_accuracy))
17 train_step.run(feed_dict={x: array_batch_xs, y_: batch_ys, keep_prob: 0.5})
InvalidArgumentError: Input to reshape is a tensor with 8000000 values, but the requested shape requires a multiple of 480000
متن کامل خطا در لینک زیر:
https://pastebin.com/ZAuJaXrj
لطفا راهنمایی بفرمایید.