import numpy as np

import os

import tensorflow as tf

import matplotlib

import matplotlib.pyplot as plt

from sklearn.datasets import load_sample_image

def reset_graph(seed=42):

tf.reset_default_graph()

tf.set_random_seed(seed)

np.random.seed(seed)

plt.rcParams['axes.labelsize'] = 14

plt.rcParams['xtick.labelsize'] = 12

plt.rcParams['ytick.labelsize'] = 12

plt.rcParams['font.family'] = 'NanumBarunGothic'

plt.rcParams['axes.unicode_minus'] = False

PROJECT_ROOT_DIR = "."

CHAPTER_ID = "cnn"

def save_fig(fig_id, tight_layout=True):

plt.savefig(path, format='png', dpi=300)

def plot_image(image):

plt.imshow(image, cmap="gray", interpolation="nearest")

plt.axis("off")

def plot_color_image(image):

plt.imshow(image.astype(np.uint8), interpolation="nearest")

plt.axis("off")

china = load_sample_image("china.jpg")

flower = load_sample_image("flower.jpg")

dataset = np.array([china, flower], dtype=np.float32)

batch_size, height, width, channels = dataset.shape

print(china.shape)

print(dataset.shape)

filters = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)

# filter

filters[:, 3, :, 0] = 1 # 수직

filters[3, :, :, 1] = 1 # 수평

X = tf.placeholder(tf.float32, shape=(None, height, width, channels))

# convolution = tf.nn.conv2d(X, filters, strides=[1, 2, 2, 1], padding="SAME")

convolution = tf.layers.conv2d(X, filters=2, kernel_size=7, strides=[2, 2], padding="SAME")

max_pool = tf.nn.max_pool(X, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")

init = tf.global_variables_initializer()

with tf.Session() as sess:

init.run()

output = sess.run(convolution, feed_dict={X: dataset})

pooling = sess.run(max_pool, feed_dict={X: dataset})

print(pooling.shape)

# plt.imshow(output[1, :, :, 1], cmap="gray")

# plt.show()

plot_color_image(pooling[0])

plt.show()

plot_color_image(dataset[0])

plt.show()

for image_index in (0, 1):

for feature_map_index in (0, 1):

plt.imshow(output[image_index, :, :, feature_map_index], cmap="gray")

plt.show()