Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
import sys
import tensorflow as tf
def predictint(imvalue):
"""
This function returns the predicted integer.
The imput is the pixel values from the imageprepare() function.
"""
# Define the model (same as when creating the model file)
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
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, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, 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([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(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
init_op = tf.initialize_all_variables()
saver = tf.train.Saver()
"""
Load the model2.ckpt file
file is stored in the same directory as this python script is started
Use the model to predict the integer. Integer is returend as list.
Based on the documentatoin at
https://www.tensorflow.org/versions/master/how_tos/variables/index.html
"""
with tf.Session() as sess:
sess.run(init_op)
saver.restore(sess, "model2.ckpt")
#print ("Model restored.")
prediction=tf.argmax(y_conv,1)
return prediction.eval(feed_dict={x: [imvalue],keep_prob: 1.0}, session=sess)
def main(argv):
"""
Main function.
"""
imvalue = imageprepare(argv)
predint = predictint(imvalue)
print (predint[0]) #first value in list
if __name__ == "__main__":
main(sys.argv[1])
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
You can’t perform that action at this time.