matplotlib example cnn model 1 (snippet)

Python matplotlib example 'cnn model 1'

Functions in program:

def max_pooling2d(_input):
def conv2d(_input, filters):

Modules used in program:

import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

python cnn model 1

Python matplotlib example: cnn model 1

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import os
%matplotlib inline

image_path = './Test_Dataset_png'
image_file_paths = [os.path.join(image_path, filename) 
              for filename in os.listdir(image_path)]

label_path = ['./label_csv/Label.csv']

tf.reset_default_graph()
image_filename_queue = tf.train.string_input_producer(image_file_paths, shuffle=False)
label_filename_queue = tf.train.string_input_producer(label_path, shuffle=False)

image_reader = tf.WholeFileReader()
label_reader = tf.TextLineReader()

image_key, image_value = image_reader.read(image_filename_queue)
label_key, label_value = label_reader.read(label_filename_queue)

decoded_img = tf.image.decode_png(image_value)
decoded_csv = tf.decode_csv(label_value, record_defaults=[[0]])

decoded_img = tf.reshape(decoded_img, [61, 49, 1])

batch_size = 20
x, y_ = tf.train.batch(
    [decoded_img, decoded_csv], 
    batch_size=batch_size)

x = tf.cast(x, tf.float32)
y_ = tf.cast(x, tf.float32)

def conv2d(_input, filters):
    return tf.layers.conv2d(_input,
                           filters=filters,
                            kernel_size=3,
                            activation=tf.nn.relu,
                            padding="SAME")

def max_pooling2d(_input):
    return tf.layers.max_pooling2d(_input,
                                  pool_size=2,
                                  strides=[2, 2])

conv1 = conv2d(x, 10)
print(conv1.shape)
pool1 = max_pooling2d(conv1)
print(pool1.shape)
conv2 = conv2d(pool1, 20)
print(conv2.shape)
pool2 = max_pooling2d(conv2)

Python links

Learn Python: https://pythonbasics.org/
Python Tutorial: https://pythonprogramminglanguage.com