deep_conv_pool_drop.py

"""This module implements a deep conv classifier with max pool and dropout."""
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten, MaxPool2D, Dropout
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from loss_plot import loss_plot

# Number of epochs
epochs = 20
# Batchsize
batch_size = 128
# Optimizer for the generator
from keras.optimizers import Adam
optimizer = Adam(lr=0.0001)
# Shape of the input image
input_shape = (28,28,1)

(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train, X_val, y_train, y_val = train_test_split(X_train, y_train,
                                                  stratify = y_train,
                                                  test_size = 0.08333,
                                                  random_state=42)

X_train = X_train.reshape(-1,28,28,1)
X_val = X_val.reshape(-1,28,28,1)
X_test = X_test.reshape(-1,28,28,1)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3,3), input_shape=input_shape,
                 activation = 'relu'))
model.add(MaxPool2D(2,2))
model.add(Dropout(0.2))
model.add(Conv2D(64, kernel_size=(3,3), activation = 'relu'))
model.add(MaxPool2D(2,2))
model.add(Dropout(0.2))
model.add(Conv2D(128, kernel_size=(3,3), activation = 'relu'))
model.add(MaxPool2D(2,2))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation = 'softmax'))

model.compile(loss = 'sparse_categorical_crossentropy', optimizer= optimizer,
              metrics = ['accuracy'])

history = model.fit(X_train, y_train, epochs = epochs, batch_size=batch_size,
                    validation_data=(X_val, y_val))

loss,acc = model.evaluate(X_test, y_test)
print('Test loss:', loss)
print('Accuracy:', acc)

loss_plot(history)