def init_nn():
learning_rate = 0.001
nn = Sequential(learning_rate=learning_rate,
epochs=50,
batch_size=100,
learning_rate_decay=0.95,
weight_decay=0.001)
nn.add(Dense(n=200, in_shape=train.shape[1]))
nn.add(BatchNorm())
nn.add(Dense(n=100))
nn.add(BatchNorm())
nn.add(Dense(n=80))
nn.add(BatchNorm())
nn.add(Dense(n=20))
nn.add(BatchNorm())
nn.add(Dense(n=80))
nn.add(BatchNorm())
nn.add(Dense(n=100))
nn.add(BatchNorm())
nn.add(Dense(n=200))
nn.add(BatchNorm())
nn.add(Dense(n=10, activation="softmax"))
nn.compile(loss="cross_entropy_softmax", optimiser="Adam")
return nn
def model_SGD(X, y, verbose):
nn = Sequential(learning_rate=learning_rate*0.5, epochs=epochs, batch_size=100,
learning_rate_decay=0.95, weight_decay=0.01)
nn.add(Dense(n=200, in_shape=X.shape[1]))
nn.add(BatchNorm())
nn.add(Dense(n=100))
nn.add(BatchNorm())
nn.add(Dense(n=80))
nn.add(BatchNorm())
nn.add(Dense(n=40))
nn.add(BatchNorm())
nn.add(Dense(n=80))
nn.add(BatchNorm())
nn.add(Dense(n=100))
nn.add(BatchNorm())
nn.add(Dense(n=200))
nn.add(BatchNorm())
nn.add(Dense(n=10, activation="softmax"))
nn.compile(loss="cross_entropy_softmax", optimiser="SGD")
nn.fit(X, y, verbose)
return nn
learning_rate = 0.001
nn = Sequential(learning_rate=learning_rate,
epochs=100,
batch_size=100,
learning_rate_decay=0.95,
weight_decay=0.001)
nn.add(Dense(n=200, in_shape=train.shape[1]))
nn.add(BatchNorm())
nn.add(Dense(n=100))
nn.add(BatchNorm())
nn.add(Dense(n=200))
nn.add(BatchNorm())
nn.add(Dense(n=10, activation="softmax"))
nn.compile(loss="cross_entropy_softmax", optimiser="Adam")
indices = list(range(len(train)))
random.shuffle(indices)
train = list(map(train.__getitem__, indices))
label = list(map(label.__getitem__, indices))
X = scale_data(train)
y = np.array(onehot_labels(label), dtype=np.float64)
nn.fit(X[0:50000], y[0:50000], verbose=True)
print('Accuracy is: ' + str(accuracy(nn, X[50000:], label[50000:])))