mpl.py

"""This module implements a simple multi layer perceptron in keras."""
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
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, 784)
X_val = X_val.reshape(-1, 784)
X_test = X_test.reshape(-1, 784)

model = Sequential()
model.add(Dense(300, input_shape=(784,), activation = 'relu'))
model.add(Dense(300, activation='relu'))
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)