In [1]:
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
In [2]:
sess = tf.InteractiveSession()
image = np.array([[[[1],[2],[3]],
[[4],[5],[6]],
[[7],[8],[9]]]], dtype=np.float32)
print(image.shape)
plt.imshow(image.reshape(3,3), cmap='Greys')
(1, 3, 3, 1)
Out[2]:
<matplotlib.image.AxesImage at 0x10db29e10>
1 filter (2,2,1,1) with padding: VALID¶
weight.shape = 1 filter (2 , 2 , 1, 1)
In [3]:
# print("imag:\n", image)
print("image.shape", image.shape)
weight = tf.constant([[[[1.]],[[1.]]],
[[[1.]],[[1.]]]])
print("weight.shape", weight.shape)
conv2d = tf.nn.conv2d(image, weight, strides=[1, 1, 1, 1], padding='VALID')
conv2d_img = conv2d.eval()
print("conv2d_img.shape", conv2d_img.shape)
conv2d_img = np.swapaxes(conv2d_img, 0, 3)
for i, one_img in enumerate(conv2d_img):
print(one_img.reshape(2,2))
plt.subplot(1,2,i+1), plt.imshow(one_img.reshape(2,2), cmap='gray')
image.shape (1, 3, 3, 1) weight.shape (2, 2, 1, 1) conv2d_img.shape (1, 2, 2, 1) [[ 12. 16.] [ 24. 28.]]
1 filter (2,2,1,1) with padding:SAME¶
In [4]:
# print("imag:\n", image)
print("image.shape", image.shape)
weight = tf.constant([[[[1.]],[[1.]]],
[[[1.]],[[1.]]]])
print("weight.shape", weight.shape)
conv2d = tf.nn.conv2d(image, weight, strides=[1, 1, 1, 1], padding='SAME')
conv2d_img = conv2d.eval()
print("conv2d_img.shape", conv2d_img.shape)
conv2d_img = np.swapaxes(conv2d_img, 0, 3)
for i, one_img in enumerate(conv2d_img):
print(one_img.reshape(3,3))
plt.subplot(1,2,i+1), plt.imshow(one_img.reshape(3,3), cmap='gray')
image.shape (1, 3, 3, 1) weight.shape (2, 2, 1, 1) conv2d_img.shape (1, 3, 3, 1) [[ 12. 16. 9.] [ 24. 28. 15.] [ 15. 17. 9.]]
3 filters (2,2,1,3)¶
In [5]:
# print("imag:\n", image)
print("image.shape", image.shape)
weight = tf.constant([[[[1.,10.,-1.]],[[1.,10.,-1.]]],
[[[1.,10.,-1.]],[[1.,10.,-1.]]]])
print("weight.shape", weight.shape)
conv2d = tf.nn.conv2d(image, weight, strides=[1, 1, 1, 1], padding='SAME')
conv2d_img = conv2d.eval()
print("conv2d_img.shape", conv2d_img.shape)
conv2d_img = np.swapaxes(conv2d_img, 0, 3)
for i, one_img in enumerate(conv2d_img):
print(one_img.reshape(3,3))
plt.subplot(1,3,i+1), plt.imshow(one_img.reshape(3,3), cmap='gray')
image.shape (1, 3, 3, 1) weight.shape (2, 2, 1, 3) conv2d_img.shape (1, 3, 3, 3) [[ 12. 16. 9.] [ 24. 28. 15.] [ 15. 17. 9.]] [[ 120. 160. 90.] [ 240. 280. 150.] [ 150. 170. 90.]] [[-12. -16. -9.] [-24. -28. -15.] [-15. -17. -9.]]
In [6]:
image = np.array([[[[4],[3]],
[[2],[1]]]], dtype=np.float32)
pool = tf.nn.max_pool(image, ksize=[1, 2, 2, 1],
strides=[1, 1, 1, 1], padding='VALID')
print(pool.shape)
print(pool.eval())
(1, 1, 1, 1) [[[[ 4.]]]]
SAME: Zero paddings¶
In [7]:
image = np.array([[[[4],[3]],
[[2],[1]]]], dtype=np.float32)
pool = tf.nn.max_pool(image, ksize=[1, 2, 2, 1],
strides=[1, 1, 1, 1], padding='SAME')
print(pool.shape)
print(pool.eval())
(1, 2, 2, 1) [[[[ 4.] [ 3.]] [[ 2.] [ 1.]]]]
In [8]:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# Check out https://www.tensorflow.org/get_started/mnist/beginners for
# more information about the mnist dataset
Extracting MNIST_data/train-images-idx3-ubyte.gz Extracting MNIST_data/train-labels-idx1-ubyte.gz Extracting MNIST_data/t10k-images-idx3-ubyte.gz Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
In [9]:
img = mnist.train.images[0].reshape(28,28)
plt.imshow(img, cmap='gray')
Out[9]:
<matplotlib.image.AxesImage at 0x1152b2048>
In [10]:
sess = tf.InteractiveSession()
img = img.reshape(-1,28,28,1)
W1 = tf.Variable(tf.random_normal([3, 3, 1, 5], stddev=0.01))
conv2d = tf.nn.conv2d(img, W1, strides=[1, 2, 2, 1], padding='SAME')
print(conv2d)
sess.run(tf.global_variables_initializer())
conv2d_img = conv2d.eval()
conv2d_img = np.swapaxes(conv2d_img, 0, 3)
for i, one_img in enumerate(conv2d_img):
plt.subplot(1,5,i+1), plt.imshow(one_img.reshape(14,14), cmap='gray')
Tensor("Conv2D_3:0", shape=(1, 14, 14, 5), dtype=float32)
In [11]:
pool = tf.nn.max_pool(conv2d, ksize=[1, 2, 2, 1], strides=[
1, 2, 2, 1], padding='SAME')
print(pool)
sess.run(tf.global_variables_initializer())
pool_img = pool.eval()
pool_img = np.swapaxes(pool_img, 0, 3)
for i, one_img in enumerate(pool_img):
plt.subplot(1,5,i+1), plt.imshow(one_img.reshape(7, 7), cmap='gray')
Tensor("MaxPool_2:0", shape=(1, 7, 7, 5), dtype=float32)
