The convolutional neural network is one of the most popular deep learning model for image classification. In this Blog, we are designing a CNN model to classify the Cat Vs Dog (weather image is lable is cat or dog) using Tensorflow.
This is a simple CNN Network
# Importing the libraries import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator
#data sugmentation # Preprocessing the Training set train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') training_set = train_datagen.flow_from_directory('image_data/training', target_size = (64, 64), batch_size = 32, class_mode = 'binary')
Found 198 images belonging to 2 classes.
# Preprocessing the Test set test_datagen = ImageDataGenerator(rescale = 1./255) test_set = test_datagen.flow_from_directory('image_data/validation', target_size = (64, 64), batch_size = 32, class_mode = 'binary')
Found 100 images belonging to 2 classes.
## showing some image from training import matplotlib.pyplot as plt def plotImages(images_arr): fig, axes = plt.subplots(1, 5, figsize=(20, 20)) axes = axes.flatten() for img, ax in zip(images_arr, axes): ax.imshow(img) plt.tight_layout() plt.show()
images = [training_set[0][0][0] for i in range(5)] plotImages(images)
Model Build Use Only CNN
from tensorflow.keras.layers import Conv2D
# Part 2 - Building the CNN # Initialising the CNN cnn = tf.keras.models.Sequential() # Step 1 - # Adding a first convolutional layer cnn.add(tf.keras.layers.Conv2D(filters=32,padding="same",kernel_size=3, activation='relu', ## step 2 - #apply maxpool cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2)) ## Apply pooing stride # Adding a second convolutional layer cnn.add(tf.keras.layers.Conv2D(filters=32,padding='same',kernel_size=3, activation='relu')) cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2)) # Step 3 - Flattening cnn.add(tf.keras.layers.Flatten()) # Step 4 - Full Connection cnn.add(tf.keras.layers.Dense(units=128, activation='relu')) tf.keras.layers.Dropout(0.5) # Step 5 - Output Layer cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))
# Part 3 - Training the CNN # Compiling the CNN cnn.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Training the CNN on the Training set and evaluating it on the Test set history = cnn.fit(x = training_set, validation_data = test_set, epochs = 2)
![](https://buffml.com/wp-content/uploads/2021/04/epoch-cnn.png)
Save And Load Model
#save model from tensorflow.keras.models import load_model cnn.save('model.h5')
from tensorflow.keras.models import load_model # load model model = load_model('model.h5')
# Part 4 - Making a single prediction import numpy as np from tensorflow.keras.preprocessing import image test_image = image.load_img('image_data/test/3285.jpg', target_size = (64,64)) test_image = image.img_to_array(test_image) test_image=test_image/255 test_image = np.expand_dims(test_image, axis = 0) result = cnn.predict(test_image) result
array([[0.5059088]], dtype=float32)
if result[0]<=0.5: print("The image classified is cat") else: print("The image classified is dog") from IPython.display import Image Image(filename='image_data/test/3285.jpg',height='200',width='200')
The image classified is dog
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