Keras - Convolution Neural Network

Let us modify the model from MPL to Convolution Neural Network (CNN) for our earlier digit identification problem.

CNN can be represented as below −

Convolution Neural Network

The core features of the model are as follows −

  • Input layer consists of (1, 8, 28) values.

  • First layer, Conv2D consists of 32 filters and ‘relu’ activation function with kernel size, (3,3).

  • Second layer, Conv2D consists of 64 filters and ‘relu’ activation function with kernel size, (3,3).

  • Thrid layer, MaxPooling has pool size of (2, 2).

  • Fifth layer, Flatten is used to flatten all its input into single dimension.

  • Sixth layer, Dense consists of 128 neurons and ‘relu’ activation function.

  • Seventh layer, Dropout has 0.5 as its value.

  • Eighth and final layer consists of 10 neurons and ‘softmax’ activation function.

  • Use categorical_crossentropy as loss function.

  • Use Adadelta() as Optimizer.

  • Use accuracy as metrics.

  • Use 128 as batch size.

  • Use 20 as epochs.

Step 1 − Import the modules

Let us import the necessary modules.

import keras 
from keras.datasets import mnist 
from keras.models import Sequential 
from keras.layers import Dense, Dropout, Flatten 
from keras.layers import Conv2D, MaxPooling2D 
from keras import backend as K 
import numpy as np

Step 2 − Load data

Let us import the mnist dataset.

(x_train, y_train), (x_test, y_test) = mnist.load_data()

Step 3 − Process the data

Let us change the dataset according to our model, so that it can be feed into our model.

img_rows, img_cols = 28, 28 

if K.image_data_format() == 'channels_first': 
   x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) 
   x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) 
   input_shape = (1, img_rows, img_cols) 
else: 
   x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) 
   x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) 
   input_shape = (img_rows, img_cols, 1) 
   
x_train = x_train.astype('float32') 
x_test = x_test.astype('float32') 
x_train /= 255 
x_test /= 255 

y_train = keras.utils.to_categorical(y_train, 10) 
y_test = keras.utils.to_categorical(y_test, 10)

The data processing is similar to MPL model except the shape of the input data and image format configuration.

Step 4 − Create the model

Let us create tha actual model.

model = Sequential() 
model.add(Conv2D(32, kernel_size = (3, 3),  
   activation = 'relu', input_shape = input_shape)) 
model.add(Conv2D(64, (3, 3), activation = 'relu')) 
model.add(MaxPooling2D(pool_size = (2, 2))) 
model.add(Dropout(0.25)) model.add(Flatten()) 
model.add(Dense(128, activation = 'relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(10, activation = 'softmax'))

Step 5 − Compile the model

Let us compile the model using selected loss function, optimizer and metrics.

model.compile(loss = keras.losses.categorical_crossentropy, 
   optimizer = keras.optimizers.Adadelta(), metrics = ['accuracy'])

Step 6 − Train the model

Let us train the model using fit() method.

model.fit(
   x_train, y_train, 
   batch_size = 128, 
   epochs = 12, 
   verbose = 1, 
   validation_data = (x_test, y_test)
)

Executing the application will output the below information −

Train on 60000 samples, validate on 10000 samples Epoch 1/12 
60000/60000 [==============================] - 84s 1ms/step - loss: 0.2687 
- acc: 0.9173 - val_loss: 0.0549 - val_acc: 0.9827 Epoch 2/12 
60000/60000 [==============================] - 86s 1ms/step - loss: 0.0899 
- acc: 0.9737 - val_loss: 0.0452 - val_acc: 0.9845 Epoch 3/12 
60000/60000 [==============================] - 83s 1ms/step - loss: 0.0666 
- acc: 0.9804 - val_loss: 0.0362 - val_acc: 0.9879 Epoch 4/12 
60000/60000 [==============================] - 81s 1ms/step - loss: 0.0564 
- acc: 0.9830 - val_loss: 0.0336 - val_acc: 0.9890 Epoch 5/12 
60000/60000 [==============================] - 86s 1ms/step - loss: 0.0472 
- acc: 0.9861 - val_loss: 0.0312 - val_acc: 0.9901 Epoch 6/12 
60000/60000 [==============================] - 83s 1ms/step - loss: 0.0414 
- acc: 0.9877 - val_loss: 0.0306 - val_acc: 0.9902 Epoch 7/12 
60000/60000 [==============================] - 89s 1ms/step - loss: 0.0375 
-acc: 0.9883 - val_loss: 0.0281 - val_acc: 0.9906 Epoch 8/12 
60000/60000 [==============================] - 91s 2ms/step - loss: 0.0339 
- acc: 0.9893 - val_loss: 0.0280 - val_acc: 0.9912 Epoch 9/12 
60000/60000 [==============================] - 89s 1ms/step - loss: 0.0325 
- acc: 0.9901 - val_loss: 0.0260 - val_acc: 0.9909 Epoch 10/12 
60000/60000 [==============================] - 89s 1ms/step - loss: 0.0284 
- acc: 0.9910 - val_loss: 0.0250 - val_acc: 0.9919 Epoch 11/12 
60000/60000 [==============================] - 86s 1ms/step - loss: 0.0287 
- acc: 0.9907 - val_loss: 0.0264 - val_acc: 0.9916 Epoch 12/12 
60000/60000 [==============================] - 86s 1ms/step - loss: 0.0265 
- acc: 0.9920 - val_loss: 0.0249 - val_acc: 0.9922

Step 7 − Evaluate the model

Let us evaluate the model using test data.

score = model.evaluate(x_test, y_test, verbose = 0) 

print('Test loss:', score[0]) 
print('Test accuracy:', score[1])

Executing the above code will output the below information −

Test loss: 0.024936060590433316 
Test accuracy: 0.9922

The test accuracy is 99.22%. We have created a best model to identify the handwriting digits.

Step 8 − Predict

Finally, predict the digit from images as below −

pred = model.predict(x_test) 
pred = np.argmax(pred, axis = 1)[:5] 
label = np.argmax(y_test,axis = 1)[:5] 

print(pred) 
print(label)

The output of the above application is as follows −

[7 2 1 0 4] 
[7 2 1 0 4]

The output of both array is identical and it indicate our model correctly predicts the first five images.

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