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another test by using real photo.py
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another test by using real photo.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Oct 20 15:15:13 2017
@author: KAI
"""
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
def CNN(tf_x, tf_y):
cnn1 = tf.layers.conv2d(
inputs=tf_x,
filters=16, # shape(96,96,16)
kernel_size=5,
strides=1,
padding='same',
activation=tf.nn.relu)
pool1 = tf.layers.max_pooling2d(
cnn1,
pool_size=2,
strides=2) # 池化=压缩->shape(48,48,16)
cnn2 = tf.layers.conv2d(pool1, 32, 5, 1, 'same', activation=tf.nn.relu) # (48,48,32)
pool2 = tf.layers.max_pooling2d(cnn2, 2, 2) # (24,24,32)
cnn3 = tf.layers.conv2d(pool2, 64, 3, 1, 'same', activation=tf.nn.relu) # (24,24,64)
pool3 = tf.layers.max_pooling2d(cnn3, 2, 2) # (12,12,64)
cnn4 = tf.layers.conv2d(pool3, 128, 3, 1, 'same', activation=tf.nn.relu) # (12,12,128)
pool4 = tf.layers.max_pooling2d(cnn4, 2, 2) # (6,6,128)
output_previous = tf.layers.dense(tf.reshape(pool4, [-1, 6 * 6 * 128]), 500)
# output_second=tf.layers.dense(output_previous,500)
output = tf.layers.dense(output_previous, 30)
loss = tf.losses.mean_squared_error(tf_y, output)
# loss=tf.losses.sparse_softmax_cross_entropy(labels=tf_y,logits=output)
# accuracy=tf.metrics.accuracy(labels=tf_y,predictions=tf.argmax(output,axis=1),)[1]
return output, loss
if __name__=='__main__':
X=np.zeros((4,96,96,1))
a=plt.imread('IMG_3526.png')
X[0,:,:,:]=a[:,:,1,np.newaxis]
a=plt.imread('IMG_3572.png')
X[1,:,:,:]=a[:,:,1,np.newaxis]
a=plt.imread('IMG_3927.png')
X[2,:,:,:]=a[:,:,1,np.newaxis]
a=plt.imread('IMG_3948.png')
X[3,:,:,:]=a[:,:,1,np.newaxis]
tf_x = tf.placeholder(tf.float32, [None, 96, 96, 1])
tf_y = tf.placeholder(tf.float32, [None, 30])
output, loss = CNN(tf_x, tf_y)
sess_ = tf.Session()
saver_ = tf.train.Saver()
saver_.restore(sess_, './facial data/save_CNN.ckpt')
pred_test = sess_.run(output, feed_dict={tf_x: X})
x_img = pred_test[:, ::2] * 96.0
y_img = pred_test[:, 1::2] * 96.0
_, p = plt.subplots(1, 4, figsize=(4, 1))
for j in range(4):
p[j].imshow(np.squeeze(X[j,:,:,:]))
p[j].scatter(x_img[j], y_img[j], c='r')
plt.show()