机器之心今天的情绪，是怎样一段待你解开的 Python 代码：
将上图输入以下卷积网络，你想要的都在这~
import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim
import urllib.request
OUTPUT_NODE = 36
IMAGE_SIZE = 200
NUM_CHANNELS = 1
CONV1_SIZE = 2
CONV2_SIZE = 3
FC_SIZE = 512
w = 0.44480515
W = np.array([[57, 20.5, -19.33333206, -5.75, -7.20000076, -13.16666603],
[2., 21.5, 7., -3.75
, -8., -12.83333397],
[2., 28., 7., -22., -9.20000076, -13.83333397],
[88., 20.5, -19.33333206, -5.75, -8., -24.66666603],
[67., 25., 6.66666794, -0.75, -10.60000038, -12.],
[2., 26., 2.33333206, -1.5, -6.79999924, -9.83333397]]).astype(np.float32)
def inference(input_tensor):
with tf.variable_scope('layer1-conv1'):
conv1_weights = tf.get_variable('weight', [CONV1_SIZE, CONV1_SIZE, 1, 1],
initializer=tf.constant_initializer(W[0:2, 0:2]))
conv1_biases = tf.get_variable('bias', [1], initializer=tf.constant_initializer(0.0))
conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')
relu1 = tf.nn.sigmoid(tf.nn.bias_add(conv1, conv1_biases))
with tf.name_scope('layer2-pool1'):
pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
with tf.variable_scope('layer3-conv2'):
conv2_weights = tf.get_variable('weight', [CONV2_SIZE, CONV2_SIZE, 1, 1],
initializer=tf.constant_initializer(W[0:3, 0:3]))
conv2_biases = tf.get_variable('bias', [1], initializer=tf.constant_initializer(0.0))
conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')
relu2 = tf.nn.sigmoid(tf.nn.bias_add(conv2, conv2_biases))
with tf.name_scope('layer4-pool2'):
pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], activation_fn=tf.nn.sigmoid, stride=1, padding='SAME'):
with tf.variable_scope('layer5-Inception_v3-Module'):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(pool2, 1, [1, 1],
weights_initializer=tf.constant_initializer(W[3:4, 3:4
]), scope='Ince_0')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(pool2, 1, [1, 1],
weights_initializer=tf.constant_initializer(W[4:5, 4:5]), scope='Ince_1_1')
branch_1 = tf.concat([slim.conv2d(branch_1, 32, [1, 3],
weights_initializer=tf.constant_initializer(W[3:4, 1:4]), scope='Ince_1_2a'),
slim.conv2d(branch_1, 32, [3, 1],
weights_initializer=tf.constant_initializer(W[1:4, 3:4]), scope='Ince_1_2b')], 3)
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(pool2, 1, [1, 1],
weights_initializer=tf.constant_initializer(W[4:5, 4:5]), scope='Ince_2_1')
branch_2 = slim.conv2d(branch_2, 1, [3, 3],
weights_initializer=tf.constant_initializer(W[0:3, 0:3]), scope='Ince_2_2')
branch_2 = tf.concat([slim.conv2d(branch_2, 1, [1, 3],
weights_initializer=tf.constant_initializer(W[0:1, 0:3]), scope='Ince_2_3a'),
slim.conv2d(branch_2, 1, [3, 1],
weights_initializer=tf.constant_initializer(W[0:3, 0:1]), scope='Ince_2_3b')], 3)
with tf.variable_scope('Branch_3'):
branch_3 = slim.conv2d(pool2, 1, [1, 1],
weights_initializer=tf.constant_initializer(W[4:5, 4:5]), scope='Ince_3_2')
inception = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
inception_shape = inception.get_shape().as_list()
nodes = inception_shape[1] * inception_shape[2] * inception_shape[3]
reshaped = tf.reshape(inception, [1, nodes])
with tf.variable_scope('layer6-fc1'):
fc1_weights = tf.get_variable('weight', [nodes, FC_SIZE],
initializer=tf.truncated_normal_initializer(stddev=3, seed=3), trainable=False)
fc1_biases = tf.get_variable('bias', [FC_SIZE], initializer=tf.constant_initializer(-10.0))
fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)
with tf.variable_scope('layer7-fc2'):
fc2_weights = tf.get_variable('weight', [FC_SIZE, OUTPUT_NODE],
initializer=tf.constant_initializer(0.0001))
fc2_biases = tf.get_variable('bias', [OUTPUT_NODE], initializer=tf.constant_initializer(-11.0))
secret = tf.matmul(fc1, fc2_weights) + fc2_biases
return secret
def synced(image):
img_data = tf.image.decode_jpeg(image)
resized = tf.image.resize_images(img_data, [IMAGE_SIZE, IMAGE_SIZE], method=1)
img_gray = tf.reshape(tf.image.rgb_to_grayscale(resized), [1, IMAGE_SIZE, IMAGE_SIZE, 1])
img_norm = tf.cast(img_gray/128-1, dtype=tf.float32)
y_hat = tf.reshape(inference(img_norm), [6, 6]) - w
y_norm = tf.matmul(W+30, y_hat + tf.cast(tf.diag([1, 2, 3, 4, 5, 6]), dtype=tf.float32))
y_int = tf.reshape(tf.cast(y_norm, dtype=tf.int16), [1, 36])
c=[]
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
y = sess.run(y_int)
for i in range(OUTPUT_NODE):
c.append(chr(abs(y[0][i])))
print("".join(c))
def main(argv=None):
urllib.request.urlretrieve(
'https://image.jiqizhixin.com/uploads/editor/051635e7-a31d-44d8-a97e-b34da37ddbbc/82418Synced.jpg',
'Synced.jpg')
img_raw = tf.gfile.FastGFile("./Synced.jpg", "rb").read()
synced(img_raw)
if __name__ == '__main__':
tf.app.run()
# Source Code:https://github.com/jiqizhixin/ML-Tutorial-Experiment/blob/master/Experiments/Synced.py
「Life is short, you need Python」
