In [23]:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('.', one_hot=True)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
In [4]:
import tensorflow as tf
sess = tf.InteractiveSession()
sess
Out[4]:
In [20]:
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')
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
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.matmul(h_fc1_drop, W_fc2) + b_fc2
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_))
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))
sess.run(tf.global_variables_initializer())
In [22]:
for i in range(2000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print("test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
In [72]:
print(range(sess.run(W_conv1).shape[-1]))
w = sess.run(W_conv1)
# from matplotlib import colors
for i in range(w.shape[-1]):
plt.figure(figsize=[0.5,0.5])
plt.imshow(w[:, :, 0, i], cmap='gray')
plt.show()
# plt.imshow(feat[:, :, 1])
In [98]:
print(sess.run(W_conv2).shape)
w2 = sess.run(W_conv2)
# from matplotlib import colors
for i in range(w2.shape[-1]):
plt.figure(figsize=[0.5,0.5])
plt.imshow(w[:, :, 0, i], cmap='gray')
plt.show()
In [83]:
img = 1 - mpimg.imread('digit.png').reshape([784, 3])[:, 1]
h = sess.run(h_conv1, feed_dict={x: [img], keep_prob: 1})
for i in range(h.shape[-1]):
plt.imshow(h[0, :, :, i], cmap='gray')
plt.show()
In [88]:
img = 1 - mpimg.imread('digit.png').reshape([784, 3])[:, 1]
h = sess.run(h_conv2, feed_dict={x: [img], keep_prob: 1})
print(h.shape)
for i in range(h.shape[-1]):
plt.imshow(h[0, :, :, i], cmap='gray')
plt.show()
In [94]:
img = 1 - mpimg.imread('digit.png').reshape([784, 3])[:, 1]
h = sess.run(h_fc1, feed_dict={x: [img], keep_prob: 1}).reshape(32, 32)
plt.imshow(h, cmap='gray')
plt.show()
In [103]:
def match(img):
# print(mnist.test.images[0])
print(sess.run(y_conv, feed_dict={x: [img], keep_prob: 1}))
print(sess.run(tf.argmax(y_conv, 1), feed_dict={x: [img], keep_prob: 1}))
plt.figure(figsize=[4,4])
plt.imshow(np.split(img, 28), cmap='gray')
plt.show()
match(1 - mpimg.imread('digit.png').reshape([784, 3])[:, 1])
match(1 - mpimg.imread('data.png').reshape([784, 3])[:, 1])
match(1 - mpimg.imread('data1.png').reshape([784, 3])[:, 1])
match(1 - mpimg.imread('seven.png').reshape([784, 3])[:, 1])
match(mnist.test.images[4])