def main():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
print 'Imported MNIST data: training input %s and training labels %s.' % (
x_train.shape, y_train.shape)
print 'Imported MNIST data: test input %s and test labels %s.' % (
x_test.shape, y_test.shape)
N, H, W = x_train.shape
x = x_train.reshape((N, H * W)).astype('float') / 255
y = to_categorical(y_train, num_classes=10)
model = Sequential()
model.add(Dense(), ReLU(), layer_dim=(28 * 28, 300), weight_scale=1e-2)
model.add(Dense(), ReLU(), layer_dim=(300, 100), weight_scale=1e-2)
model.add(Dense(), Softmax(), layer_dim=(100, 10), weight_scale=1e-2)
model.compile(optimizer=GradientDescent(learning_rate=1e-2),
loss_func=categorical_cross_entropy)
model.fit(x, y, epochs=10, batch_size=50, verbose=False)
N, H, W = x_test.shape
x = x_test.reshape((N, H * W)).astype('float') / 255
y = to_categorical(y_test, num_classes=10)
model.evaluate(x, y)
def linear_classification(a=1.0, b=0.0, graph=False):
# prepare data
x = np.linspace(-100, 100, 200)
y = a * x + b
X = np.array(list(zip(x, y))) + np.random.randn(200, 2) * 100
Y = to_one_hot(np.where(a * X[:, 0] + b > X[:, 1], 1, 0))
(train_x, train_y), (test_x, test_y) = split_data(X,
Y,
ratio=0.8,
random=True)
# build simple FNN
i = Input(2)
x = Dense(2, activation='softmax')(i)
# define trainer
trainer = Trainer(loss='cross_entropy',
optimizer=Adam(learning_rate=0.05),
batch_size=50,
epochs=50,
metrics=['accuracy'])
# create model
model = Sequential(i, x, trainer)
model.summary()
# training process
model.fit(train_x, train_y)
print(model.evaluate(test_x, test_y))
if graph:
plt.plot(model.history['loss'])
plt.show()
# predict
y_hat = model.predict(test_x)
y_hat = np.argmax(y_hat, axis=1)
simple_plot(test_x, y_hat, a, b)
train_y = convert_to_one_hot(train_y, num_classes)
test_x = np.reshape(test_x, (len(test_x), 1, img_rows, img_cols)).astype(skml_config.config.i_type)
test_y = convert_to_one_hot(test_y, num_classes)
train_x, valid_x, train_y, valid_y = train_test_split(train_x, train_y)
filters = 64
model = Sequential()
model.add(Convolution(filters, 3, input_shape=input_shape))
model.add(BatchNormalization())
model.add(ReLU())
model.add(MaxPooling(2))
model.add(Convolution(filters, 3))
model.add(BatchNormalization())
model.add(ReLU())
model.add(GlobalAveragePooling())
model.add(Affine(num_classes))
model.compile(SoftmaxCrossEntropy(), Adam())
train_batch_size = 100
valid_batch_size = 1
print("訓練開始: {}".format(datetime.now().strftime("%Y/%m/%d %H:%M")))
model.fit(train_x, train_y, train_batch_size, 20, validation_data=(valid_batch_size, valid_x, valid_y), validation_steps=1)
print("訓練終了: {}".format(datetime.now().strftime("%Y/%m/%d %H:%M")))
model.save(save_path)
loss, acc = model.evaluate(test_x, test_y)
print("Test loss: {}".format(loss))
print("Test acc: {}".format(acc))