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5_cat_vs_dog_cnn.py
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5_cat_vs_dog_cnn.py
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import numpy as np
import os
import tensorflow as tf
work_dir = '../dogs_vs_cats_dataset/data'
image_height, image_width = 150, 150
train_dir = os.path.join(work_dir, 'train')
test_dir = os.path.join(work_dir, 'test')
no_classes = 2
no_validation = 800
epochs = 2
batch_size = 200
no_train = 2000
no_test = 800
input_shape = (image_height, image_width, 3)
epoch_steps = no_train // batch_size
test_steps = no_test // batch_size
def simple_cnn(input_shape):
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(
filters=64,
kernel_size=(3, 3),
activation='relu',
input_shape=input_shape
))
model.add(tf.keras.layers.Conv2D(
filters=128,
kernel_size=(3, 3),
activation='relu'
))
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(tf.keras.layers.Dropout(rate=0.3))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(units=1024, activation='relu'))
model.add(tf.keras.layers.Dropout(rate=0.3))
model.add(tf.keras.layers.Dense(units=no_classes, activation='softmax'))
model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.Adam(),
metrics=['accuracy'])
return model
simple_cnn_model = simple_cnn(input_shape)
generator_train = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255)
generator_test = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255)
# Download the train dataset and test dataset,
# extract them into 2 different folders named as “train” and “test”.
# The train folder should contain ‘n’ folders each containing images of respective classes.
# For example, In the Dog vs Cats data set, the train folder should have 2 folders,
# namely “Dog” and “Cats” containing respective images inside them.
train_images = generator_train.flow_from_directory(
train_dir,
batch_size=batch_size,
target_size=(image_width, image_height))
# batch_size: Set this to some number that divides your total number of images in your test set exactly.
# Why this only for test_generator?
# Actually, you should set the “batch_size” in both train and valid generators to some number
# that divides your total number of images in your train set and valid respectively,
# but this doesn’t matter before because even if batch_size doesn’t match the number
# of samples in the train or valid sets and some images gets missed out every time
# we yield the images from generator, it would be sampled the very next epoch you train.
# But for the test set, you should sample the images exactly once, no less or no more.
# If Confusing, just set it to 1(but maybe a little bit slower).
test_images = generator_test.flow_from_directory(
test_dir,
batch_size=batch_size,
target_size=(image_width, image_height))
simple_cnn_model.fit_generator(
train_images,
steps_per_epoch=epoch_steps,
epochs=epochs,
validation_data=test_images,
validation_steps=test_steps)