CNN MNIST Example using Keras
Description for CNN MNIST Example using Keras notebook.
Notebook Contents
This notebook covers:
- Topic 1
- Topic 2
- Topic 3
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Simple CNN for MNISTĀ¶
Using the MNIST dataset (70 000 pictures of hand-written digits) we will train a simple CNN, which is able to predict a digit given a picture of a hand-written digit.
Adapted from: https://github.com/kenophobio/keras-example-notebook
InĀ [1]:
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
import matplotlib.pyplot as plt
Network parameters:
InĀ [2]:
batch_size = 128
nb_classes = 10
nb_epoch = 12
# input image dimensions
img_rows, img_cols = 28, 28
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
pool_size = (2, 2)
# convolution kernel size
kernel_size = 3 #(3, 3)
Prepare data into training and test set.
InĀ [3]:
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
InĀ [11]:
plt.imshow(X_train[0])
Out[11]:
InĀ [15]:
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)
InĀ [16]:
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
InĀ [17]:
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
InĀ [35]:
plt.imshow(X_train[10][:,:,:]);
Build the CNN.
InĀ [13]:
kernel_size
Out[13]:
InĀ [18]:
model = Sequential()
model.add(Convolution2D(nb_filters, kernel_size,
padding='same',
input_shape=input_shape))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, kernel_size))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
Show a summary of the model parameters.
InĀ [8]:
model.summary()
And now train the model and evaluate on the test set.
InĀ [37]:
history = model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epoch,
verbose=1, validation_data=(X_test, Y_test))
InĀ [10]:
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
InĀ [14]:
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
InĀ [Ā ]:
def display_activation(activations, col_size, row_size, act_index):
activation = activations[act_index]
activation_index=0
fig, ax = plt.subplots(row_size, col_size, figsize=(row_size*2.5,col_size*1.5))
for row in range(0,row_size):
for col in range(0,col_size):
ax[row][col].imshow(activation[0, :, :, activation_index], cmap='gray')
activation_index += 1
#https://www.kaggle.com/amarjeet007/visualize-cnn-with-keras
InĀ [Ā ]:
#display_activation(activations, 8, 8, 1)