def main(args):
train_set, valid_set, test_set = prepare_dataset(args.data_dir)
train_x, train_y = train_set
test_x, test_y = test_set
# train_y = get_one_hot(train_y, 2)
net = Net([Dense(100), ReLU(), Dense(30), ReLU(), Dense(1)])
model = Model(net=net,
loss=SigmoidCrossEntropyLoss(),
optimizer=Adam(lr=args.lr))
iterator = BatchIterator(batch_size=args.batch_size)
evaluator = AccEvaluator()
loss_list = list()
for epoch in range(args.num_ep):
t_start = time.time()
for batch in iterator(train_x, train_y):
pred = model.forward(batch.inputs)
loss, grads = model.backward(pred, batch.targets)
model.apply_grad(grads)
loss_list.append(loss)
print("Epoch %d time cost: %.4f" % (epoch, time.time() - t_start))
for timer in model.timers.values():
timer.report()
# evaluate
model.set_phase("TEST")
test_y_idx = np.asarray(test_y).reshape(-1)
test_pred = model.forward(test_x)
test_pred[test_pred > 0] = 1
test_pred[test_pred <= 0] = 0
test_pred_idx = test_pred.reshape(-1)
res = evaluator.evaluate(test_pred_idx, test_y_idx)
print(res)
model.set_phase("TRAIN")
def main(args):
if args.seed >= 0:
random_seed(args.seed)
train_set, valid_set, test_set = prepare_dataset(args.data_dir)
train_x, train_y = train_set
test_x, test_y = test_set
train_y = get_one_hot(train_y, 10)
train_x = Tensor(train_x)
train_y = Tensor(train_y)
test_x = Tensor(test_x)
test_y = Tensor(test_y)
net = Net([
Dense(200),
ReLU(),
Dense(100),
ReLU(),
Dense(70),
ReLU(),
Dense(30),
ReLU(),
Dense(10)
])
model = Model(net=net,
loss=SoftmaxCrossEntropyLoss(),
optimizer=Adam(lr=args.lr))
loss_layer = SoftmaxCrossEntropyLoss()
iterator = BatchIterator(batch_size=args.batch_size)
evaluator = AccEvaluator()
loss_list = list()
for epoch in range(args.num_ep):
t_start = time.time()
for batch in iterator(train_x, train_y):
model.zero_grad()
pred = model.forward(batch.inputs)
loss = loss_layer.loss(pred, batch.targets)
loss.backward()
model.step()
loss_list.append(loss.values)
print("Epoch %d tim cost: %.4f" % (epoch, time.time() - t_start))
# evaluate
model.set_phase("TEST")
test_pred = model.forward(test_x)
test_pred_idx = np.argmax(test_pred, axis=1)
test_y_idx = test_y.values
res = evaluator.evaluate(test_pred_idx, test_y_idx)
print(res)
model.set_phase("TRAIN")
def main(args):
if args.seed >= 0:
random_seed(args.seed)
# create output directory for saving result images
if not os.path.exists('./output'): os.mkdir('./output')
# define network we are going to load
net = Net([
Conv2D(kernel=[5, 5, 1, 6], stride=[1, 1], padding="SAME"),
ReLU(),
MaxPool2D(pool_size=[2, 2], stride=[2, 2]),
Conv2D(kernel=[5, 5, 6, 16], stride=[1, 1], padding="SAME"),
ReLU(),
MaxPool2D(pool_size=[2, 2], stride=[2, 2]),
Flatten(),
Dense(120),
ReLU(),
Dense(84),
ReLU(),
Dense(10)
])
# load the model
model = Model(net=net, loss=SoftmaxCrossEntropyLoss(), optimizer=Adam())
print('loading pre-trained model file', args.model_path)
model.load(args.model_path)
# create pyplot window for on-the-fly visualization
img = np.ones((1, 28, 28, 1))
fig = disp_mnist_batch(img)
# actual visualization generations
layer_name = 'conv-layer-1'
print('[ ' + layer_name + ' ]')
images = am_visualize_conv_layer(model, 0, fig)
save_batch_as_images('output/{}.png'.format(layer_name),
images,
title='visualized feature maps for ' + layer_name)
layer_name = 'conv-layer-2'
print('[ ' + layer_name + ' ]')
images = am_visualize_conv_layer(model, 3, fig)
save_batch_as_images('output/{}.png'.format(layer_name),
images,
title='visualized feature maps for ' + layer_name)
def convert_dense(spec, previous_layer):
return Dense(
name=spec._get_name(),
channels=spec.units,
use_bias=spec.use_bias,
activation=spec.activation,
inbound_nodes=[previous_layer],
)
def convert_dense(identifier, spec, previous_layer):
return Dense(
name=identifier,
channels=spec.out_features,
use_bias=True,
activation='linear',
inbound_nodes=[previous_layer.name],
)
def main(args):
if args.seed >= 0:
random_seed(args.seed)
# data preparing
data_path = os.path.join(args.data_dir, args.file_name)
train_x, train_y, img_shape = prepare_dataset(data_path)
net = Net([
Dense(30),
ReLU(),
Dense(60),
ReLU(),
Dense(60),
ReLU(),
Dense(30),
ReLU(),
Dense(3),
Sigmoid()
])
model = Model(net=net, loss=MSELoss(), optimizer=Adam())
mse_evaluator = MSEEvaluator()
iterator = BatchIterator(batch_size=args.batch_size)
for epoch in range(args.num_ep):
t_start = time.time()
for batch in iterator(train_x, train_y):
preds = model.forward(batch.inputs)
loss, grads = model.backward(preds, batch.targets)
model.apply_grad(grads)
# evaluate
preds = net.forward(train_x)
mse = mse_evaluator.evaluate(preds, train_y)
print(mse)
if args.paint:
# generate painting
preds = preds.reshape(img_shape[0], img_shape[1], -1)
preds = (preds * 255.0).astype("uint8")
filename, ext = os.path.splitext(args.file_name)
output_filename = "output" + ext
output_path = os.path.join(args.data_dir, output_filename)
Image.fromarray(preds).save(output_path)
print("Epoch %d time cost: %.2f" % (epoch, time.time() - t_start))
def main(args):
train_set, valid_set, test_set = prepare_dataset(args.data_dir)
train_x, train_y = train_set
test_x, test_y = test_set
train_y = get_one_hot(train_y, 10)
if args.model_type == "cnn":
train_x = train_x.reshape((-1, 28, 28, 1))
test_x = test_x.reshape((-1, 28, 28, 1))
if args.model_type == "cnn":
net = Net([
Conv2D(kernel=[5, 5, 1, 8], stride=[2, 2], padding="SAME"),
ReLU(),
Conv2D(kernel=[5, 5, 8, 16], stride=[2, 2], padding="SAME"),
ReLU(),
Conv2D(kernel=[5, 5, 16, 32], stride=[2, 2], padding="SAME"),
ReLU(),
Flatten(),
Dense(10)
])
elif args.model_type == "dense":
net = Net([
Dense(200),
ReLU(),
Dense(100),
ReLU(),
Dense(70),
ReLU(),
Dense(30),
ReLU(),
Dense(10)
])
else:
raise ValueError(
"Invalid argument model_type! Must be 'cnn' or 'dense'")
model = Model(net=net,
loss=SoftmaxCrossEntropyLoss(),
optimizer=Adam(lr=args.lr))
iterator = BatchIterator(batch_size=args.batch_size)
evaluator = AccEvaluator()
loss_list = list()
for epoch in range(args.num_ep):
t_start = time.time()
for batch in iterator(train_x, train_y):
pred = model.forward(batch.inputs)
loss, grads = model.backward(pred, batch.targets)
model.apply_grad(grads)
loss_list.append(loss)
print("Epoch %d time cost: %.4f" % (epoch, time.time() - t_start))
# evaluate
model.set_phase("TEST")
test_pred = model.forward(test_x)
test_pred_idx = np.argmax(test_pred, axis=1)
test_y_idx = np.asarray(test_y)
res = evaluator.evaluate(test_pred_idx, test_y_idx)
print(res)
model.set_phase("TRAIN")
def main(args):
train_set, valid_set, test_set = prepare_dataset(args.data_dir)
train_x, train_y = train_set
test_x, test_y = test_set
train_y = get_one_hot(train_y, 10)
net = Net([
Dense(784, 200),
ReLU(),
Dense(200, 100),
ReLU(),
Dense(100, 70),
ReLU(),
Dense(70, 30),
ReLU(),
Dense(30, 10)
])
model = Model(net=net,
loss=SoftmaxCrossEntropyLoss(),
optimizer=Adam(lr=args.lr))
iterator = BatchIterator(batch_size=args.batch_size)
evaluator = AccEvaluator()
loss_list = list()
for epoch in range(args.num_ep):
t_start = time.time()
for batch in iterator(train_x, train_y):
pred = model.forward(batch.inputs)
loss, grads = model.backward(pred, batch.targets)
model.apply_grad(grads)
loss_list.append(loss)
t_end = time.time()
# evaluate
test_pred = model.forward(test_x)
test_pred_idx = np.argmax(test_pred, axis=1)
test_y_idx = np.asarray(test_y)
res = evaluator.evaluate(test_pred_idx, test_y_idx)
print("Epoch %d time cost: %.4f\t %s" % (epoch, t_end - t_start, res))
def build_net(self):
q_net = Net([Dense(100), ReLU(), Dense(self.action_dim)])
return q_net
def convert(keras_model, class_map, description="Neural Network Model"):
"""
Convert a keras model to PMML
@model. The keras model object
@class_map. A map in the form {class_id: class_name}
@description. A short description of the model
Returns a DeepNeuralNetwork object which can be exported to PMML
"""
pmml = DeepNetwork(description=description, class_map=class_map)
pmml.keras_model = keras_model
pmml.model_name = keras_model.name
config = keras_model.get_config()
for layer in config['layers']:
layer_class = layer['class_name']
layer_config = layer['config']
layer_inbound_nodes = layer['inbound_nodes']
# Input
if layer_class is "InputLayer":
pmml._append_layer(
InputLayer(name=layer_config['name'],
input_size=layer_config['batch_input_shape'][1:]))
# Conv2D
elif layer_class is "Conv2D":
pmml._append_layer(
Conv2D(
name=layer_config['name'],
channels=layer_config['filters'],
kernel_size=layer_config['kernel_size'],
dilation_rate=layer_config['dilation_rate'],
use_bias=layer_config['use_bias'],
activation=layer_config['activation'],
strides=layer_config['strides'],
padding=layer_config['padding'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
# DepthwiseConv2D
elif layer_class is "DepthwiseConv2D":
pmml._append_layer(
DepthwiseConv2D(
name=layer_config['name'],
kernel_size=layer_config['kernel_size'],
depth_multiplier=layer_config['depth_multiplier'],
use_bias=layer_config['use_bias'],
activation=layer_config['activation'],
strides=layer_config['strides'],
padding=layer_config['padding'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
# MaxPooling
elif layer_class is "MaxPooling2D":
pmml._append_layer(
MaxPooling2D(
name=layer_config['name'],
pool_size=layer_config['pool_size'],
strides=layer_config['strides'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
elif layer_class is "AveragePooling2D":
pmml._append_layer(
AveragePooling2D(
name=layer_config['name'],
pool_size=layer_config['pool_size'],
strides=layer_config['strides'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
elif layer_class is "GlobalAveragePooling2D":
pmml._append_layer(
GlobalAveragePooling2D(
name=layer_config['name'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
# Flatten
elif layer_class is "Flatten":
pmml._append_layer(
Flatten(
name=layer_config['name'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
# Dense
elif layer_class is "Dense":
pmml._append_layer(
Dense(
name=layer_config['name'],
channels=layer_config['units'],
use_bias=layer_config['use_bias'],
activation=layer_config['activation'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
# Zero padding layer
elif layer_class is "ZeroPadding2D":
pmml._append_layer(
ZeroPadding2D(
name=layer_config['name'],
padding=layer_config['padding'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
# Reshape layer
elif layer_class is "Reshape":
pmml._append_layer(
Reshape(
name=layer_config['name'],
target_shape=layer_config['target_shape'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
elif layer_class is "Dropout":
pmml._append_layer(
Dropout(
name=layer_config['name'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
# Batch Normalization
elif layer_class is "BatchNormalization":
pmml._append_layer(
BatchNormalization(
name=layer_config['name'],
axis=layer_config['axis'],
momentum=layer_config['momentum'],
epsilon=layer_config['epsilon'],
center=layer_config['center'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
elif layer_class is "Add":
pmml._append_layer(
Merge(name=layer_config['name'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes)))
elif layer_class is "Subtract":
pmml._append_layer(
Merge(name=layer_config['name'],
operator='subtract',
inbound_nodes=get_inbound_nodes(layer_inbound_nodes)))
elif layer_class is "Dot":
pmml._append_layer(
Merge(name=layer_config['name'],
operator='dot',
inbound_nodes=get_inbound_nodes(layer_inbound_nodes)))
elif layer_class is "Concatenate":
pmml._append_layer(
Merge(name=layer_config['name'],
axis=layer_config['axis'],
operator='concatenate',
inbound_nodes=get_inbound_nodes(layer_inbound_nodes)))
elif layer_class is "Activation":
pmml._append_layer(
Activation(
name=layer_config['name'],
activation=layer_config['activation'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
elif layer_class is "ReLU":
pmml._append_layer(
Activation(
name=layer_config['name'],
activation='relu',
threshold=layer_config['threshold'],
max_value=layer_config['max_value'],
negative_slope=layer_config['negative_slope'],
inbound_nodes=get_inbound_nodes(layer_inbound_nodes),
))
# Unknown layer
else:
raise ValueError("Unknown layer type:", layer_class)
return pmml
from core.layers import Dense, Dropout, Conv2D, Flatten, ReLU, Softmax, MaxPooling2D
from core.model import Model
from core.utils import load_mnist
from core.optimizer import SGD
from core.loss import CategoricalCrossEntropy
import matplotlib.pyplot as plt
np.random.seed(1234)
model = Model()
model.add(Conv2D(filters=1, shape=(28, 28, 1), kernel_size=(3, 3)))
model.add(ReLU())
model.add(MaxPooling2D(shape=(2, 2)))
model.add(Flatten())
model.add(Dense(shape=(169, 128))) #676
model.add(ReLU())
model.add(Dense(shape=(128, 10)))
model.add(Softmax())
model.compile(optimizer=SGD(lr=0.01), loss=CategoricalCrossEntropy())
(x, y), (x_test, y_test) = load_mnist()
x = x.reshape(x.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
train_loss_cnn, train_acc_cnn, val_loss_cnn, val_acc_cnn = model.fit(
x, y, x_test, y_test, epoch=10, batch_size=32)
plt.plot(train_acc_cnn, label='cnn train accuracy')
plt.plot(val_acc_cnn, label='cnn val accuracy')
import numpy as np from core.layers import Dense, Dropout, ReLU, Softmax from core.model import Model from core.utils import load_mnist from core.optimizer import SGD from core.loss import CategoricalCrossEntropy import matplotlib.pyplot as plt np.random.seed(1234) model = Model() model.add(Dense(shape=(784, 512))) model.add(ReLU()) model.add(Dropout(0.2)) model.add(Dense(shape=(512, 512))) model.add(ReLU()) model.add(Dropout(0.2)) model.add(Dense(shape=(512, 10))) model.add(Softmax()) model.compile(optimizer=SGD(lr=0.001), loss=CategoricalCrossEntropy()) (x, y), (x_test, y_test) = load_mnist() train_loss, train_acc, val_loss, val_acc = model.fit(x, y, x_test, y_test, epoch=10, batch_size=128) plt.plot(train_acc, label='train loss')
def main(args):
if args.seed >= 0:
random_seed(args.seed)
# create output directory for saving result images
if not os.path.exists('./output'):
os.mkdir('./output')
# prepare and read dataset
train_set, valid_set, test_set = prepare_dataset(args.data_dir)
train_x, train_y = train_set
test_x, test_y = test_set
# batch iterator
iterator = BatchIterator(batch_size=args.batch_size)
# specify the encoder and decoder net structure
encoder = Net([Dense(256), ReLU(), Dense(64)])
decoder = Net([ReLU(), Dense(256), Tanh(), Dense(784), Tanh()])
# create AutoEncoder model
model = AutoEncoder(encoder=encoder,
decoder=decoder,
loss=MSELoss(),
optimizer=Adam(args.lr))
# for pretrained model, test generated images from latent space
if args.load_model is not None:
# load pretrained model
model.load(args.load_model)
print('Loaded model from %s' % args.load_model)
# transition from test[from_idx] to test[to_idx] in n steps
idx_arr, n = [2, 4, 32, 12, 82], 160
print("Transition in numbers", [test_y[i] for i in idx_arr],
"in %d steps ..." % n)
stops = [model.encoder.forward(test_x[i]) for i in idx_arr]
k = int(n / (len(idx_arr) - 1)) # number of code per transition
# generate all transition codes
code_arr = []
for i in range(len(stops) - 1):
t = [c.copy() for c in transition(stops[i], stops[i + 1], k)]
code_arr += t
# apply decoding all n "code" from latent space...
batch = None
for code in code_arr:
# translate latent space to image
genn = model.decoder.forward(code)
# save decoded results in a batch
if batch is None:
batch = np.array(genn)
else:
batch = np.concatenate((batch, genn))
save_batch_as_images('output/genn-latent.png', batch)
quit()
# train the autoencoder
for epoch in range(args.num_ep):
print('epoch %d ...' % epoch)
for batch in iterator(train_x, train_y):
origin_in = batch.inputs
# make noisy inputs
m = origin_in.shape[0] # batch size
mu = args.guassian_mean # mean
sigma = args.guassian_std # standard deviation
noises = np.random.normal(mu, sigma, (m, 784))
noises_in = origin_in + noises # noisy inputs
# train the representation
genn = model.forward(noises_in)
loss, grads = model.backward(genn, origin_in)
model.apply_grad(grads)
print('Loss: %.3f' % loss)
# save all the generated images and original inputs for this batch
save_batch_as_images('output/ep%d-input.png' % epoch,
noises_in,
titles=batch.targets)
save_batch_as_images('output/ep%d-genn.png' % epoch,
genn,
titles=batch.targets)
# save the model after training
model.save('output/model.pkl')