BT4221 Assignment 4 Question 2

question2.py

# https://paper.dropbox.com/doc/Assignment-4--AQAaS_sEpPtBSwlJT9IK8u3qAg-AbLDwYkKF1jWLBcH7aM0J
from __future__ import print_function

import keras
from keras.datasets import mnist
from keras.models import Model
from keras.layers import Input, Dense, Flatten
from keras.layers import Dense, Activation
from keras.layers import SimpleRNN
from keras import initializers
from keras.optimizers import RMSprop

batch_size = 32
num_classes = 10
epochs = 10
#hidden_units = 100

#learning_rate = 1e-6

# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(x_train.shape[0], -1, 1)
x_test = x_test.reshape(x_test.shape[0], -1, 1)
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')

# Convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

print('Evaluate RNN...')
#'''
# Create Model
input_layer = Input(shape=(784,1)) #input layer
#layer = Dense(10, activation='tanh')(input_layer) #hidden layer
#output_layer = Dense(1, activation = 'sigmoid')(layer) #output layer
#output_layer = Dense(784,)(input_layer)
layers= Dense(10, activation='tanh')(input_layer)
layers = Flatten()(layers)
output_layer=Dense(10, activation='sigmoid')(layers)
model = Model(inputs=input_layer, outputs= output_layer) #i get an error

# Compile Model
model.compile(loss='categorical_crossentropy',
              optimizer='Adagrad',
              metrics=['accuracy'])

# Fit Model
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))

# Evaluate Model
scores = model.evaluate(x_test, y_test, verbose=0)
print('RNN test score:', scores[0])
print('RNN test accuracy:', scores[1])