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]) |
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