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trainCNN.py
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trainCNN.py
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# Loading libraries:
import numpy
import matplotlib.pyplot as plt
from keras.datasets import mnist
import keras
from keras.layers import Dense, Conv2D,Dropout,MaxPooling2D,Flatten
from keras.models import Sequential
img_rows, img_cols = 28, 28
num_classes = 10
# Load & Reshaping Dataset:
def load_dataset():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000,28,28,1)
x_test = x_test.reshape(10000,28,28,1)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
return x_train, x_test, y_train, y_test
x_train, x_test, y_train, y_test = load_dataset()
# Define Model:
def define_model():
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
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'))
model.compile(optimizer=keras.optimizers.Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])
print(model.summary())
return model
model = define_model()
# Train Model:
model.fit(x_train, y_train, validation_data=(x_test, y_test),verbose=1, epochs=25, batch_size=128)
#Evaluate Model:
score = model.evaluate(x_test, y_test, verbose=0)
print('Loss :', score[0] , '%')
print('Accuracy :', score[1]*100, '%')
#Save Model:
model.save('CNN_Model.h5')