3. CNN - changing the Hyperparameters

 AIM:

Description:

Program:

# Convolutional Neural Network

# Installing Theano

# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git

# Installing Tensorflow

# Install Tensorflow from the website: https://www.tensorflow.org/versions/r0.12/get_started/os_setup.html

# Installing Keras

# pip install --upgrade keras

# Part 1 - Building the CNN

# Importing the Keras libraries and packages

from keras.models import Sequential

from keras.layers import Convolution2D

from keras.layers import MaxPooling2D

from keras.layers import Flatten

from keras.layers import Dense

# Initialising the CNN

classifier = Sequential()

# Step 1 - Convolution

classifier.add(Convolution2D(32, 3, 3, input_shape = (64, 64, 3), activation = 'relu'))

# Step 2 - Pooling

classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer

classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))

classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening

classifier.add(Flatten())

# Step 4 - Full connection

classifier.add(Dense(units = 128, activation = 'relu'))

classifier.add(Dense(units = 1, activation = 'sigmoid'))

# Compiling the CNN

classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Part 2 - Fitting the CNN to the images

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,

                                   shear_range = 0.2,

                                   zoom_range = 0.2,

                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('/content/drive/MyDrive/1_training_set',

                                                 target_size = (64, 64),

                                                 batch_size = 40,

                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('/content/drive/MyDrive/1_test_set',

                                            target_size = (64, 64),

                                            batch_size = 8,

                                            class_mode = 'binary')

classifier.fit(training_set,

                         steps_per_epoch = 10,

                         epochs = 20,

                         validation_data = test_set,

                         validation_steps = 10)

import numpy as np

from tensorflow.keras.preprocessing import image

# Load the trained model

# Assuming you have already trained and saved the model as 'classifier'

model = classifier

# Load the image you want to classify

image_path = '/content/drive/MyDrive/1_predict/dog2.jpg'  # Replace with the actual path of your image

img = image.load_img(image_path, target_size=(64, 64))

# Preprocess the image

img_array = image.img_to_array(img)

img_array = np.expand_dims(img_array, axis=0)

img_array /= 255.0  # Rescale to match the normalization done during training

# Make predictions on the image

predictions = model.predict(img_array)

#predicted_class = np.argmax(predictions)

print("Predictions:", predictions)

predicted_class = np.round(predictions)

# Print the predicted class label

print("Predicted class label:", predicted_class)

if(predicted_class==0):

  print('cat')

else: print('dog')

3.B: Updated one

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**************************************************

from keras.models import Sequential

from keras.layers import Convolution2D, MaxPooling2D, Flatten, Dense, Dropout

#from keras.preprocessing.image import ImageDataGenerator

#from tensorflow.keras.models import Sequential

#from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout

from tensorflow.keras.preprocessing.image import ImageDataGenerator

import numpy as np

from tensorflow.keras.preprocessing import image

# Initialising the CNN

classifier = Sequential()

# Step 1 - Convolution

classifier.add(Convolution2D(32, (3, 3), padding='same', activation='relu', input_shape=(64, 64, 3)))

classifier.add(Convolution2D(32, (3, 3), activation='relu'))

classifier.add(MaxPooling2D(pool_size=(2, 2)))

classifier.add(Dropout(0.25))

# Adding more convolutional layers

classifier.add(Convolution2D(64, (3, 3), padding='same', activation='relu'))

classifier.add(Convolution2D(64, (3, 3), activation='relu'))

classifier.add(MaxPooling2D(pool_size=(2, 2)))

classifier.add(Dropout(0.25))

# Adding a third convolutional block

classifier.add(Convolution2D(128, (3, 3), padding='same', activation='relu'))

classifier.add(Convolution2D(128, (3, 3), activation='relu'))

classifier.add(MaxPooling2D(pool_size=(2, 2)))

classifier.add(Dropout(0.5))

# Step 3 - Flattening

classifier.add(Flatten())

# Step 4 - Full connection

classifier.add(Dense(units=512, activation='relu'))

classifier.add(Dropout(0.5))

classifier.add(Dense(units=1, activation='sigmoid'))

# Compiling the CNN

classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

train_datagen = ImageDataGenerator(

    rescale=1./255,

    shear_range=0.2,

    zoom_range=0.2,

    horizontal_flip=True,

    rotation_range=20,  # Add rotation

    width_shift_range=0.2,  # Add width shift

    height_shift_range=0.2  # Add height shift

)

# Part 2 - Fitting the CNN to the images

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('/content/drive/MyDrive/1_training_set',

                                                 target_size = (64, 64),

                                                 batch_size = 20,

                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('/content/drive/MyDrive/1_test_set',

                                            target_size = (64, 64),

                                            batch_size = 4,

                                            class_mode = 'binary')

classifier.fit(training_set,

                         steps_per_epoch = 20,

                         epochs = 25,

                         validation_data = test_set,

                         validation_steps = 10)

from tensorflow.keras.preprocessing import image

# Load the trained model

model = classifier

# Load the image you want to classify

image_path = '/content/drive/MyDrive/1_predict/cat2.jpg'

img = image.load_img(image_path, target_size=(64, 64))

# Preprocess the image

img_array = image.img_to_array(img)

img_array = np.expand_dims(img_array, axis=0)

img_array /= 255.0  # Rescale to match the normalization done during training

# Make predictions on the image

predictions = model.predict(img_array)

print("predictions", predictions)

# Convert predictions to binary

predicted_class = np.round(predictions).astype(int)

# Print the predicted class label

if predicted_class == 0:

    print('cat')

else:

    print('dog')