CNN-Colab.py

import tensorflow as tf
from tensorflow import keras
from tensorflow.python.keras.applications.inception_resnet_v2 import preprocess_input
from tensorflow.python.keras.models import load_model
from tensorflow.python.keras.applications.inception_resnet_v2 import InceptionResNetV2
from tensorflow.python.keras.applications.mobilenet import preprocess_input
from tensorflow.python.keras.applications.mobilenet import MobileNet
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import cv2
import matplotlib.pyplot as plt
import glob
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Flatten, Conv2D, Dropout, MaxPooling2D
from tensorflow.python.keras.models import Model


mobile = MobileNet()

x = mobile.layers[-6].output
prediction = Dense(2, activation="softmax")(x)
model = Model(inputs=mobile.input, outputs=prediction)

for layer in model.layers[:-5]:
  layer.trainable = False
  
image_size = 224
batch_size = 32

data_generator = ImageDataGenerator(
    width_shift_range=0.2,
    height_shift_range=0.2,
    rescale=1./255)

test_datagen = ImageDataGenerator( rescale=1./255)

train_generator = data_generator.flow_from_directory(
        './dataset/training',
        target_size=(image_size, image_size),
        batch_size=batch_size,
        class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
        './dataset/testing',
        target_size=(image_size, image_size),
        batch_size=batch_size,
        class_mode='categorical')
        
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

with tf.device('/gpu:0'):
  model.fit_generator(train_generator,
        steps_per_epoch= 8251 // batch_size,
        epochs=3,
        validation_data=validation_generator,
        validation_steps=2)