def test_lookup():
model = Sequential([ Dense(3, init=1, name='first'), Dense(2, init=2, name='second')])
model.update_signature((2,))
W1 = model.first.W.value
W2 = model.second.W.value
np.testing.assert_array_equal(W1, np.ones((2,3)), err_msg='Error in lookup of Dense parameters')
np.testing.assert_array_equal(W2, np.ones((3,2)) * 2, err_msg='Error in lookup of Dense parameters')
def create_model(input_dim):
row = sequence.input_variable(shape=input_dim)
col = sequence.input_variable(shape=input_dim)
rowh = Sequential([Embedding(opt.embed), Stabilizer(), Dropout(opt.dropout)])(row)
colh = Sequential([Embedding(opt.embed), Stabilizer(), Dropout(opt.dropout)])(col)
x = C.splice(rowh, colh, axis=-1)
x = lightlstm(opt.embed, opt.nhid)(x)
x = For(range(opt.layer-1), lambda: lightlstm(opt.nhid, opt.nhid))(x)
rowh = C.slice(x, -1, opt.nhid * 0, opt.nhid * 1)
colh = C.slice(x, -1, opt.nhid * 1, opt.nhid * 2)
row_predict = Sequential([Dropout(opt.dropout), Dense(input_dim)])(rowh)
col_predict = Sequential([Dropout(opt.dropout), Dense(input_dim)])(colh)
# variable : row label and col label
row_label = sequence.input_variable(shape=input_dim)
col_label = sequence.input_variable(shape=input_dim)
model = C.combine([row_predict, col_predict])
return {'row': row,
'col': col,
'row_label': row_label,
'col_label': col_label,
'model': model}
def test_lookup():
model = Sequential([ Dense(3, init=1, name='first'), Dense(2, init=2, name='second')])
model.update_signature((2,))
W1 = model.first.W.value
W2 = model.second.W.value
np.testing.assert_array_equal(W1, np.ones((2,3)), err_msg='Error in lookup of Dense parameters')
np.testing.assert_array_equal(W2, np.ones((3,2)) * 2, err_msg='Error in lookup of Dense parameters')
def create_model(input_sequence, label_sequence, vocab_dim, hidden_dim):
# Create the rnn that computes the latent representation for the next token.
rnn_with_latent_output = Sequential([
C.layers.Embedding(hidden_dim),
For(
range(num_layers), lambda: Sequential([
Stabilizer(),
Recurrence(LSTM(hidden_dim), go_backwards=False)
])),
])
# Apply it to the input sequence.
latent_vector = rnn_with_latent_output(input_sequence)
# Connect the latent output to (sampled/full) softmax.
if use_sampled_softmax:
weights = load_sampling_weights(token_frequencies_file_path)
smoothed_weights = np.float32(np.power(weights, alpha))
sampling_weights = C.reshape(C.Constant(smoothed_weights),
shape=(1, vocab_dim))
z, ce, errs = cross_entropy_with_sampled_softmax(
latent_vector, label_sequence, vocab_dim, hidden_dim,
softmax_sample_size, sampling_weights)
else:
z, ce, errs = cross_entropy_with_full_softmax(latent_vector,
label_sequence,
vocab_dim, hidden_dim)
return z, ce, errs
def __init__(self, input_shape, nb_actions,
gamma=0.99, explorer=LinearEpsilonAnnealingExplorer(1, 0.1, 1000000),
learning_rate=0.00025, momentum=0.95, minibatch_size=32,
memory_size=500000, train_after=10000, train_interval=4,
target_update_interval=10000, monitor=True):
self.input_shape = input_shape
self.nb_actions = nb_actions
self.gamma = gamma
self._train_after = train_after
self._train_interval = train_interval
self._target_update_interval = target_update_interval
self._explorer = explorer
self._minibatch_size = minibatch_size
self._history = History(input_shape)
self._memory = RepMem(memory_size, input_shape[1:], 4)
self._num_actions_taken = 0
self._episode_rewards, self._episode_q_means, self._episode_q_stddev = [], [], []
with default_options(activation=relu, init=he_uniform()):
self._action_value_net = Sequential([
Dense(input_shape, init=he_uniform(scale=0.01)),
Dense(input_shape),
Dense(nb_actions, activation=None, init=he_uniform(scale=0.01))])
self._action_value_net.update_signature(Tensor[input_shape])
self._target_net = self._action_value_net.clone(CloneMethod.freeze)
@Function
@Signature(post_states=Tensor[input_shape], rewards=Tensor[()], terminals=Tensor[()])
def compute_q_targets(post_states, rewards, terminals):
return element_select(
terminals,
rewards,
gamma * reduce_max(self._target_net(post_states), axis=0) + rewards,
)
@Function
@Signature(pre_states=Tensor[input_shape], actions=Tensor[nb_actions],
post_states=Tensor[input_shape], rewards=Tensor[()], terminals=Tensor[()])
def criterion(pre_states, actions, post_states, rewards, terminals):
q_targets = compute_q_targets(post_states, rewards, terminals)
q_acted = reduce_sum(self._action_value_net(pre_states) * actions, axis=0)
return huber_loss(q_targets, q_acted, 1.0)
lr_schedule = learning_rate_schedule(learning_rate, UnitType.minibatch)
m_schedule = momentum_schedule(momentum)
vm_schedule = momentum_schedule(0.999)
l_sgd = adam(self._action_value_net.parameters, lr_schedule,
momentum=m_schedule, variance_momentum=vm_schedule)
self._metrics_writer = TensorBoardProgressWriter(freq=1, log_dir='metrics', model=criterion) if monitor else None
self._learner = l_sgd
self._trainer = Trainer(criterion, (criterion, None), l_sgd, self._metrics_writer)
def model(self, x):
param1 = 500
param2 = 250
x = Dense(param1, activation=cntk.tanh)(x)
x = Dense(param1, activation=cntk.tanh)(x)
x = Dense(param1, activation=cntk.tanh)(x)
x = Sequential([(Recurrence(LSTM(param2)), Recurrence(LSTM(param2), go_backwards=True)), cntk.splice])(x)
x = Sequential([(Recurrence(LSTM(param2)), Recurrence(LSTM(param2), go_backwards=True)), cntk.splice])(x)
x = Dense(self.dim_y)(x)
return x
def create_model(output_dim):
return Sequential([
For(
range(num_layers), lambda: Sequential([
Stabilizer(),
Recurrence(LSTM(hidden_dim), go_backwards=False)
])),
Dense(output_dim)
])
def create_model():
'''
Creates the model to train
:return: Returns the last output of a sequential model using LSTMs
'''
return Sequential([
For(range(NUMBER_LAYERS),
lambda: Sequential([Recurrence(LSTM(HIDDEN_LAYER_DIMENSIONS))])),
sequence.last,
Dense(NUM_OUTPUT_CLASSES)
])
def create_network():
# Create the input and target variables
input_var = cntk.input_variable(
(sequence_length, frame_height, frame_width), name='input_var')
target_var = cntk.input_variable((num_classes, ),
is_sparse=True,
name='target_var')
input_head = cntk.slice(input_var, axis=0, begin_index=0, end_index=19)
input_tail = cntk.slice(input_var, axis=0, begin_index=1, end_index=20)
diff = input_tail - input_head
model = Sequential([
resnet_model(cntk.placeholder()),
Label('resnet'),
Dense(num_classes, name='output')
])(diff)
return {
'input': input_var,
'target': target_var,
'model': model,
'loss': cntk.cross_entropy_with_softmax(model, target_var),
'metric': cntk.classification_error(model, target_var)
}
def LSTM_sequence_classifier_net(input, num_output_classes, embedding_dim,
LSTM_dim, cell_dim):
lstm_classifier = Sequential([Embedding(embedding_dim),
Recurrence(LSTM(LSTM_dim, cell_dim)),
sequence.last,
Dense(num_output_classes)])
return lstm_classifier(input)
def test_depth_first_search_blocks(depth, prefix_count):
from cntk.layers import Sequential, Convolution, MaxPooling, Dense
from cntk.default_options import default_options
def Blocked_Dense(dim, activation=None):
dense = Dense(dim, activation=activation)
@C.layers.BlockFunction('blocked_dense', 'blocked_dense')
def func(x):
return dense(x)
return func
with default_options(activation=C.relu):
image_to_vec = Sequential([
Convolution((5, 5), 32, pad=True),
MaxPooling((3, 3), strides=(2, 2)),
Dense(10, activation=None),
Blocked_Dense(10)
])
in1 = C.input_variable(shape=(3, 256, 256), name='image')
img = image_to_vec(in1)
found = C.logging.graph.depth_first_search(img,
lambda x: True,
depth=depth)
found_str = [str(v) for v in found]
assert len(found) == sum(prefix_count.values())
for prefix, count in prefix_count.items():
assert sum(f.startswith(prefix) for f in found_str) == count
def test_htk_deserializers():
mbsize = 640
epoch_size = 1000 * mbsize
lr = [0.001]
feature_dim = 33
num_classes = 132
context = 2
os.chdir(data_path)
features_file = "glob_0000.scp"
labels_file = "glob_0000.mlf"
label_mapping_file = "state.list"
fd = HTKFeatureDeserializer(
StreamDefs(amazing_features=StreamDef(
shape=feature_dim, context=(context, context), scp=features_file)))
ld = HTKMLFDeserializer(
label_mapping_file,
StreamDefs(
awesome_labels=StreamDef(shape=num_classes, mlf=labels_file)))
reader = MinibatchSource([fd, ld])
features = C.input_variable(((2 * context + 1) * feature_dim))
labels = C.input_variable((num_classes))
model = Sequential(
[For(range(3), lambda: Recurrence(LSTM(256))),
Dense(num_classes)])
z = model(features)
ce = C.cross_entropy_with_softmax(z, labels)
errs = C.classification_error(z, labels)
learner = C.adam_sgd(z.parameters,
lr=C.learning_rate_schedule(lr, C.UnitType.sample,
epoch_size),
momentum=C.momentum_as_time_constant_schedule(1000),
low_memory=True,
gradient_clipping_threshold_per_sample=15,
gradient_clipping_with_truncation=True)
progress_printer = C.ProgressPrinter(freq=0)
trainer = C.Trainer(z, (ce, errs), learner, progress_printer)
input_map = {
features: reader.streams.amazing_features,
labels: reader.streams.awesome_labels
}
# just run and verify it doesn't crash
for i in range(3):
mb_data = reader.next_minibatch(mbsize, input_map=input_map)
trainer.train_minibatch(mb_data)
assert True
os.chdir(abs_path)
def create_model(self):
mean_removed_features = minus(self.input,
constant(114),
name='mean_removed_input')
with default_options(activation=None, pad=True, bias=True):
self.model = Sequential([
Convolution2D((11, 11),
96,
init=normal(0.01),
pad=False,
name='conv1'),
Activation(activation=relu, name='relu1'),
self.__local_response_normalization(1.0,
2,
0.0001,
0.75,
name='norm1'),
MaxPooling((3, 3), (2, 2), name='pool1'),
Convolution2D((5, 5),
192,
init=normal(0.01),
init_bias=0.1,
name='conv2'),
Activation(activation=relu, name='relu2'),
self.__local_response_normalization(1.0,
2,
0.0001,
0.75,
name='norm2'),
MaxPooling((3, 3), (2, 2), name='pool2'),
Convolution2D((3, 3), 384, init=normal(0.01), name='conv3'),
Activation(activation=relu, name='relu3'),
Convolution2D((3, 3),
384,
init=normal(0.01),
init_bias=0.1,
name='conv4'),
Activation(activation=relu, name='relu4'),
Convolution2D((3, 3),
256,
init=normal(0.01),
init_bias=0.1,
name='conv5'),
Activation(activation=relu, name='relu5'),
MaxPooling((3, 3), (2, 2), name='pool5'),
Dense(4096, init=normal(0.005), init_bias=0.1, name='fc6'),
Activation(activation=relu, name='relu6'),
Dropout(0.5, name='drop6'),
Dense(4096, init=normal(0.005), init_bias=0.1, name='fc7'),
Activation(activation=relu, name='relu7'),
Dropout(0.5, name='drop7'),
Dense(self.number_labels, init=normal(0.01), name='fc8')
])(mean_removed_features)
def create_alexnet():
# Input variables denoting the features and label data
feature_var = input_variable((num_channels, image_height, image_width))
label_var = input_variable((num_classes))
# apply model to input
# remove mean value
input = minus(feature_var, constant(114), name='mean_removed_input')
with default_options(activation=None, pad=True, bias=True):
z = Sequential([
# we separate Convolution and ReLU to name the output for feature extraction (usually before ReLU)
Convolution2D((11,11), 96, init=normal(0.01), pad=False, strides=(4,4), name='conv1'),
Activation(activation=relu, name='relu1'),
LocalResponseNormalization(1.0, 2, 0.0001, 0.75, name='norm1'),
MaxPooling((3,3), (2,2), name='pool1'),
Convolution2D((5,5), 192, init=normal(0.01), init_bias=0.1, name='conv2'),
Activation(activation=relu, name='relu2'),
LocalResponseNormalization(1.0, 2, 0.0001, 0.75, name='norm2'),
MaxPooling((3,3), (2,2), name='pool2'),
Convolution2D((3,3), 384, init=normal(0.01), name='conv3'),
Activation(activation=relu, name='relu3'),
Convolution2D((3,3), 384, init=normal(0.01), init_bias=0.1, name='conv4'),
Activation(activation=relu, name='relu4'),
Convolution2D((3,3), 256, init=normal(0.01), init_bias=0.1, name='conv5'),
Activation(activation=relu, name='relu5'),
MaxPooling((3,3), (2,2), name='pool5'),
Dense(4096, init=normal(0.005), init_bias=0.1, name='fc6'),
Activation(activation=relu, name='relu6'),
Dropout(0.5, name='drop6'),
Dense(4096, init=normal(0.005), init_bias=0.1, name='fc7'),
Activation(activation=relu, name='relu7'),
Dropout(0.5, name='drop7'),
Dense(num_classes, init=normal(0.01), name='fc8')
])(input)
# loss and metric
ce = cross_entropy_with_softmax(z, label_var)
pe = classification_error(z, label_var)
pe5 = classification_error(z, label_var, topN=5)
log_number_of_parameters(z) ; print()
return {
'feature': feature_var,
'label': label_var,
'ce' : ce,
'pe' : pe,
'pe5': pe5,
'output': z
}
def create_model(input_dim, output_dim, hidden_dim, feature_input):
"""
Create a model with the layers library.
"""
my_model = Sequential ([
Dense(hidden_dim, tanh),
Dense(output_dim)
])
netout = my_model(feature_input)
return(netout)
def ffnet(learner, trainer=None):
inputs = 5
outputs = 3
layers = 2
hidden_dimension = 3
if trainer is None:
# input variables denoting the features and label data
features = C.input_variable((inputs), np.float32)
label = C.input_variable((outputs), np.float32)
# Instantiate the feedforward classification model
my_model = Sequential([
Dense(hidden_dimension,
activation=C.sigmoid,
init=C.glorot_uniform(seed=98052)),
Dense(outputs, init=C.glorot_uniform(seed=98052))
])
z = my_model(features)
ce = C.cross_entropy_with_softmax(z, label)
pe = C.classification_error(z, label)
# Instantiate the trainer object to drive the model training
progress_printer = ProgressPrinter(0)
trainer = C.Trainer(z, (ce, pe), [learner(z.parameters)],
[progress_printer])
else:
features = trainer.loss_function.arguments[0]
label = trainer.loss_function.arguments[1]
# Get minibatches of training data and perform model training
minibatch_size = 25
num_minibatches_to_train = 100
aggregate_loss = 0.0
for i in range(num_minibatches_to_train):
train_features, labels = generate_random_data(minibatch_size, inputs,
outputs)
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
trainer.train_minibatch({features: train_features, label: labels})
sample_count = trainer.previous_minibatch_sample_count
aggregate_loss += trainer.previous_minibatch_loss_average * sample_count
last_avg_error = aggregate_loss / trainer.total_number_of_samples_seen
test_features, test_labels = generate_random_data(minibatch_size, inputs,
outputs)
avg_error = trainer.test_minibatch({
features: test_features,
label: test_labels
})
print(' error rate on an unseen minibatch: {}'.format(avg_error))
return last_avg_error, avg_error, trainer
def clone_conv_layers(base_model):
if not globalvars['train_conv']:
conv_layers = clone_model(base_model, [feature_node_name], [last_conv_node_name], CloneMethod.freeze)
elif feature_node_name == start_train_conv_node_name:
conv_layers = clone_model(base_model, [feature_node_name], [last_conv_node_name], CloneMethod.clone)
else:
fixed_conv_layers = clone_model(base_model, [feature_node_name], [start_train_conv_node_name],
CloneMethod.freeze)
train_conv_layers = clone_model(base_model, [start_train_conv_node_name], [last_conv_node_name],
CloneMethod.clone)
conv_layers = Sequential([fixed_conv_layers, train_conv_layers])
return conv_layers
def create_convnet_cifar10_model(num_classes):
with default_options(activation=relu, pad=True):
return Sequential([
For(
range(2), lambda: [
Convolution2D((3, 3), 64),
Convolution2D((3, 3), 64),
MaxPooling((3, 3), strides=2)
]),
For(range(2), lambda i: [Dense([256, 128][i]),
Dropout(0.5)]),
Dense(num_classes, activation=None)
])
def test_sequential_constructor(input_data):
x = C.input_variable(len(input_data))
np_data = np.asarray(input_data, np.float32)
seq_layers = Sequential([abs, sqrt, square, cos])(x)
assert seq_layers.shape == x.shape
res = seq_layers(np_data)
expected_res = np.cos(np.square(np.sqrt(np.abs(np_data))))
np.testing.assert_array_almost_equal(res[0], expected_res, decimal=4)
def ffnet():
inputs = 3
outputs = 3
layers = 2
hidden_dimension = 3
# input variables denoting the features and label data
features = C.input((inputs), np.float32)
label = C.input((outputs), np.float32)
# Instantiate the feedforward classification model
my_model = Sequential(
[Dense(hidden_dimension, activation=C.sigmoid),
Dense(outputs)])
z = my_model(features)
ce = C.cross_entropy_with_softmax(z, label)
pe = C.classification_error(z, label)
# Instantiate the trainer object to drive the model training
lr_per_minibatch = learning_rate_schedule(0.125, UnitType.minibatch)
progress_printer = ProgressPrinter(0)
trainer = C.Trainer(z, (ce, pe), [
sgd(z.parameters,
lr=lr_per_minibatch,
gaussian_noise_injection_std_dev=0.01)
], [progress_printer])
# Get minibatches of training data and perform model training
minibatch_size = 25
num_minibatches_to_train = 100
aggregate_loss = 0.0
for i in range(num_minibatches_to_train):
train_features, labels = generate_random_data(minibatch_size, inputs,
outputs)
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
trainer.train_minibatch({features: train_features, label: labels})
sample_count = trainer.previous_minibatch_sample_count
aggregate_loss += trainer.previous_minibatch_loss_average * sample_count
last_avg_error = aggregate_loss / trainer.total_number_of_samples_seen
test_features, test_labels = generate_random_data(minibatch_size, inputs,
outputs)
avg_error = trainer.test_minibatch({
features: test_features,
label: test_labels
})
print(' error rate on an unseen minibatch: {}'.format(avg_error))
return last_avg_error, avg_error
def __init__(self, n_in, n_out, init_lr, momentum):
self.param1 = 512
self.param2 = 256
self.n_in = int(n_in)
self.n_out = int(n_out)
self.input = C.sequence.input_variable(shape=(self.n_in, ))
self.label = C.sequence.input_variable(shape=(self.n_out, ))
self.three_dnn = Sequential([
Dense(self.param1, activation=C.tanh),
Dense(self.param1, activation=C.tanh),
Dense(self.param1, activation=C.tanh)
])
self.rnn_layer1 = Sequential([(Recurrence(LSTM(self.param2)),
Recurrence(LSTM(self.param2),
go_backwards=True)),
C.splice])
self.rnn_layer2 = Sequential([(Recurrence(LSTM(self.param2)),
Recurrence(LSTM(self.param2),
go_backwards=True)),
C.splice])
self.final_dnn = Dense(self.n_out)
self.output = self.model(self.input)
self.loss = loss_fun(self.output, self.label)
self.eval_err = loss_fun(self.output, self.label)
self.lr_s = C.learning_rate_schedule(init_lr, C.UnitType.sample)
self.mom_s = C.momentum_schedule(momentum)
self.learner = C.momentum_sgd(self.output.parameters,
lr=self.lr_s,
momentum=self.mom_s)
self.trainer = C.Trainer(self.output, (self.loss, self.eval_err),
[self.learner])
def create_model(feature_dimensions, classes):
with default_options(activation=relu, init=glorot_uniform()):
model = Sequential([
For(
range(3), lambda i: [
Convolution((5, 5), [32, 32, 64][i], pad=True),
BatchNormalization(map_rank=1),
MaxPooling((3, 3), strides=(2, 2))
]),
Dense(64),
BatchNormalization(map_rank=1),
Dense(len(classes), activation=None)
])
return model(feature_dimensions)
def clone_conv_layers(base_model, cfg):
feature_node_name = cfg["MODEL"].FEATURE_NODE_NAME
start_train_conv_node_name = cfg["MODEL"].START_TRAIN_CONV_NODE_NAME
last_conv_node_name = cfg["MODEL"].LAST_CONV_NODE_NAME
if not cfg.TRAIN_CONV_LAYERS:
conv_layers = clone_model(base_model, [feature_node_name], [last_conv_node_name], CloneMethod.freeze)
elif feature_node_name == start_train_conv_node_name:
conv_layers = clone_model(base_model, [feature_node_name], [last_conv_node_name], CloneMethod.clone)
else:
fixed_conv_layers = clone_model(base_model, [feature_node_name], [start_train_conv_node_name],
CloneMethod.freeze)
train_conv_layers = clone_model(base_model, [start_train_conv_node_name], [last_conv_node_name],
CloneMethod.clone)
conv_layers = Sequential([fixed_conv_layers, train_conv_layers])
return conv_layers
def ffnet(optimizer, num_minibatches_to_train, learning_rate_func, lr_args,
learner_kwargs):
inputs = 2
outputs = 2
hidden_dimension = 50
# input variables denoting the features and label data
features = C.input_variable((inputs), np.float32)
label = C.input_variable((outputs), np.float32)
# Instantiate the feedforward classification model
my_model = Sequential([
Dense(hidden_dimension,
activation=C.sigmoid,
init=C.glorot_uniform(seed=SEED)),
Dense(outputs, init=C.glorot_uniform(seed=SEED))
])
z = my_model(features)
ce = C.cross_entropy_with_softmax(z, label)
pe = C.classification_error(z, label)
# Instantiate the trainer object to drive the model training
lr = learning_rate_func(0.125, *lr_args)
progress_printer = ProgressPrinter(0)
learner = optimizer(z.parameters, lr) if optimizer != sgd else sgd(
z.parameters, lr, **learner_kwargs)
trainer = C.Trainer(z, (ce, pe), [learner], progress_printer)
# Get minibatches of training data and perform model training
minibatch_size = 25
for i in range(num_minibatches_to_train):
train_features, labels = generate_random_data(minibatch_size, inputs,
outputs)
# Specify the mapping of input variables in the model to actual
# minibatch data to be trained with
trainer.train_minibatch({features: train_features, label: labels})
test_features, test_labels = generate_random_data(minibatch_size, inputs,
outputs)
avg_error = trainer.test_minibatch({
features: test_features,
label: test_labels
})
print(' error rate on an unseen minibatch: {}'.format(avg_error))
return z.parameters
def create_vgg16(feature_var, num_classes, dropout=0.9):
with default_options(activation=None, pad=True, bias=True):
z = Sequential([
# we separate Convolution and ReLU to name the output for feature
# extraction (usually before ReLU)
For(
range(2), lambda i: [
Convolution2D((3, 3), 64, name='conv1_{}'.format(i)),
Activation(activation=relu, name='relu1_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool1'),
For(
range(2), lambda i: [
Convolution2D((3, 3), 128, name='conv2_{}'.format(i)),
Activation(activation=relu, name='relu2_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool2'),
For(
range(3), lambda i: [
Convolution2D((3, 3), 256, name='conv3_{}'.format(i)),
Activation(activation=relu, name='relu3_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool3'),
For(
range(3), lambda i: [
Convolution2D((3, 3), 512, name='conv4_{}'.format(i)),
Activation(activation=relu, name='relu4_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool4'),
For(
range(3), lambda i: [
Convolution2D((3, 3), 512, name='conv5_{}'.format(i)),
Activation(activation=relu, name='relu5_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool5'),
Dense(4096, name='fc6'),
Activation(activation=relu, name='relu6'),
Dropout(dropout, name='drop6'),
Dense(4096, name='fc7'),
Activation(activation=relu, name='relu7'),
Dropout(dropout, name='drop7'),
Dense(num_classes, name='fc8')
])(feature_var)
return z
def ffnet():
input_dim = 2
num_output_classes = 2
num_hidden_layers = 2
hidden_layers_dim = 50
# Input variables denoting the features and label data
feature = input_variable((input_dim), np.float32)
label = input_variable((num_output_classes), np.float32)
netout = Sequential([
For(range(num_hidden_layers),
lambda i: Dense(hidden_layers_dim, activation=sigmoid)),
Dense(num_output_classes)
])(feature)
ce = cross_entropy_with_softmax(netout, label)
pe = classification_error(netout, label)
lr_per_minibatch = learning_parameter_schedule(0.5)
# Instantiate the trainer object to drive the model training
learner = sgd(netout.parameters, lr=lr_per_minibatch)
progress_printer = ProgressPrinter(128)
trainer = Trainer(netout, (ce, pe), learner, progress_printer)
# Get minibatches of training data and perform model training
minibatch_size = 25
for i in range(1024):
features, labels = generate_random_data(minibatch_size, input_dim,
num_output_classes)
# Specify the mapping of input variables in the model to actual
# minibatch data to be trained with
trainer.train_minibatch({feature: features, label: labels})
trainer.summarize_training_progress()
test_features, test_labels = generate_random_data(minibatch_size,
input_dim,
num_output_classes)
avg_error = trainer.test_minibatch({
feature: test_features,
label: test_labels
})
return avg_error
def create_recurrent_network():
# Input variables denoting the features and label data
features = sequence.input_variable(((2*context+1)*feature_dim))
labels = sequence.input_variable((num_classes))
# create network
model = Sequential([For(range(3), lambda : Recurrence(LSTM(256))),
Dense(num_classes)])
z = model(features)
ce = cross_entropy_with_softmax(z, labels)
errs = classification_error (z, labels)
return {
'feature': features,
'label': labels,
'ce' : ce,
'errs' : errs,
'output': z
}
def create_advanced_model(input, out_dims):
with default_options(activation=relu):
model = Sequential([
For(range(2), lambda i: [ # lambda with one parameter
Convolution((3,3), [32,64][i], pad=True), # depth depends on i
Convolution((5,5), [32,64][i], pad=True),
Convolution((9,9), [32,64][i], pad=True),
MaxPooling((3,3), strides=(2,2))
]),
For(range(2), lambda : [ # lambda without parameter
Dense(512),
Dropout(0.5)
]),
Dense(out_dims, activation=None)
])
output_layer=model(input)
return output_layer
def test_depth_first_search_blocks(depth, prefix_count):
from cntk.layers import Sequential, Convolution, MaxPooling, Dense
from cntk.default_options import default_options
with default_options(activation=relu):
image_to_vec = Sequential([
Convolution((5, 5), 32, pad=True),
MaxPooling((3, 3), strides=(2, 2)),
Dense(10, activation=None)
])
in1 = input(shape=(3, 256, 256), name='image')
img = image_to_vec(in1)
found = depth_first_search(img, lambda x: True, depth=depth)
found_str = [str(v) for v in found]
assert len(found) == sum(prefix_count.values())
for prefix, count in prefix_count.items():
assert sum(f.startswith(prefix) for f in found_str) == count
def ffnet(optimizer):
inputs = 2
outputs = 2
layers = 2
hidden_dimension = 50
# input variables denoting the features and label data
features = C.input_variable((inputs), np.float32)
label = C.input_variable((outputs), np.float32)
# Instantiate the feedforward classification model
my_model = Sequential ([
Dense(hidden_dimension, activation=C.sigmoid, init=C.glorot_uniform(seed=98052)),
Dense(outputs, init=C.glorot_uniform(seed=98052))])
z = my_model(features)
ce = C.cross_entropy_with_softmax(z, label)
pe = C.classification_error(z, label)
# Instantiate the trainer object to drive the model training
lr_per_minibatch = learning_rate_schedule(0.125, UnitType.minibatch)
trainer = C.Trainer(z, (ce, pe), [optimizer(z.parameters, lr_per_minibatch)])
# Get minibatches of training data and perform model training
minibatch_size = 25
num_minibatches_to_train = 63
pp = ProgressPrinter(0)
for i in range(num_minibatches_to_train):
train_features, labels = generate_random_data(minibatch_size, inputs, outputs)
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
trainer.train_minibatch({features : train_features, label : labels})
pp.update_with_trainer(trainer)
last_avg_error = pp.avg_loss_since_start()
test_features, test_labels = generate_random_data(minibatch_size, inputs, outputs)
avg_error = trainer.test_minibatch({features : test_features, label : test_labels})
print(' error rate on an unseen minibatch: {}'.format(avg_error))
return z.parameters
def create_network():
input_var = cntk.sequence.input_variable((num_channels, frame_height, frame_width), name='input_var')
target_var = cntk.input_variable((num_classes,), is_sparse=True, name='target_var')
with cntk.layers.default_options(enable_self_stabilization=True):
model = Sequential([
resnet_model(cntk.placeholder()), Label('resnet'),
Dense(hidden_dim, name='cnn_fc'),
cntk.layers.Stabilizer(),
bidirectional_recurrence(LSTM(hidden_dim // 2), LSTM(hidden_dim // 2)),
cntk.sequence.last,
BatchNormalization(),
Dense(num_classes)
])(input_var)
return {
'input': input_var,
'target': target_var,
'model': model,
'loss': cntk.cross_entropy_with_softmax(model, target_var),
'metric': cntk.classification_error(model, target_var)
}
class DeepQAgent(object):
"""
Implementation of Deep Q Neural Network agent like in:
Nature 518. "Human-level control through deep reinforcement learning" (Mnih & al. 2015)
"""
def __init__(self, input_shape, nb_actions,
gamma=0.99, explorer=LinearEpsilonAnnealingExplorer(1, 0.1, 1000000),
learning_rate=0.00025, momentum=0.95, minibatch_size=32,
memory_size=500000, train_after=10000, train_interval=4, target_update_interval=10000,
monitor=True):
self.input_shape = input_shape
self.nb_actions = nb_actions
self.gamma = gamma
self._train_after = train_after
self._train_interval = train_interval
self._target_update_interval = target_update_interval
self._explorer = explorer
self._minibatch_size = minibatch_size
self._history = History(input_shape)
self._memory = ReplayMemory(memory_size, input_shape[1:], 4)
self._num_actions_taken = 0
# Metrics accumulator
self._episode_rewards, self._episode_q_means, self._episode_q_stddev = [], [], []
# Action Value model (used by agent to interact with the environment)
with default_options(activation=relu, init=he_uniform()):
self._action_value_net = Sequential([
Convolution2D((8, 8), 16, strides=4),
Convolution2D((4, 4), 32, strides=2),
Convolution2D((3, 3), 32, strides=1),
Dense(256, init=he_uniform(scale=0.01)),
Dense(nb_actions, activation=None, init=he_uniform(scale=0.01))
])
self._action_value_net.update_signature(Tensor[input_shape])
# Target model used to compute the target Q-values in training, updated
# less frequently for increased stability.
self._target_net = self._action_value_net.clone(CloneMethod.freeze)
# Function computing Q-values targets as part of the computation graph
@Function
@Signature(post_states=Tensor[input_shape], rewards=Tensor[()], terminals=Tensor[()])
def compute_q_targets(post_states, rewards, terminals):
return element_select(
terminals,
rewards,
gamma * reduce_max(self._target_net(post_states), axis=0) + rewards,
)
# Define the loss, using Huber Loss (more robust to outliers)
@Function
@Signature(pre_states=Tensor[input_shape], actions=Tensor[nb_actions],
post_states=Tensor[input_shape], rewards=Tensor[()], terminals=Tensor[()])
def criterion(pre_states, actions, post_states, rewards, terminals):
# Compute the q_targets
q_targets = compute_q_targets(post_states, rewards, terminals)
# actions is a 1-hot encoding of the action done by the agent
q_acted = reduce_sum(self._action_value_net(pre_states) * actions, axis=0)
# Define training criterion as the Huber Loss function
return huber_loss(q_targets, q_acted, 1.0)
# Adam based SGD
lr_schedule = learning_rate_schedule(learning_rate, UnitType.minibatch)
m_schedule = momentum_schedule(momentum)
vm_schedule = momentum_schedule(0.999)
l_sgd = adam(self._action_value_net.parameters, lr_schedule,
momentum=m_schedule, variance_momentum=vm_schedule)
self._metrics_writer = TensorBoardProgressWriter(freq=1, log_dir='metrics', model=criterion) if monitor else None
self._learner = l_sgd
self._trainer = Trainer(criterion, (criterion, None), l_sgd, self._metrics_writer)
def act(self, state):
""" This allows the agent to select the next action to perform in regard of the current state of the environment.
It follows the terminology used in the Nature paper.
Attributes:
state (Tensor[input_shape]): The current environment state
Returns: Int >= 0 : Next action to do
"""
# Append the state to the short term memory (ie. History)
self._history.append(state)
# If policy requires agent to explore, sample random action
if self._explorer.is_exploring(self._num_actions_taken):
action = self._explorer(self.nb_actions)
else:
# Use the network to output the best action
env_with_history = self._history.value
q_values = self._action_value_net.eval(
# Append batch axis with only one sample to evaluate
env_with_history.reshape((1,) + env_with_history.shape)
)
self._episode_q_means.append(np.mean(q_values))
self._episode_q_stddev.append(np.std(q_values))
# Return the value maximizing the expected reward
action = q_values.argmax()
# Keep track of interval action counter
self._num_actions_taken += 1
return action
def observe(self, old_state, action, reward, done):
""" This allows the agent to observe the output of doing the action it selected through act() on the old_state
Attributes:
old_state (Tensor[input_shape]): Previous environment state
action (int): Action done by the agent
reward (float): Reward for doing this action in the old_state environment
done (bool): Indicate if the action has terminated the environment
"""
self._episode_rewards.append(reward)
# If done, reset short term memory (ie. History)
if done:
# Plot the metrics through Tensorboard and reset buffers
if self._metrics_writer is not None:
self._plot_metrics()
self._episode_rewards, self._episode_q_means, self._episode_q_stddev = [], [], []
# Reset the short term memory
self._history.reset()
# Append to long term memory
self._memory.append(old_state, action, reward, done)
def train(self):
""" This allows the agent to train itself to better understand the environment dynamics.
The agent will compute the expected reward for the state(t+1)
and update the expected reward at step t according to this.
The target expectation is computed through the Target Network, which is a more stable version
of the Action Value Network for increasing training stability.
The Target Network is a frozen copy of the Action Value Network updated as regular intervals.
"""
agent_step = self._num_actions_taken
if agent_step >= self._train_after:
if (agent_step % self._train_interval) == 0:
pre_states, actions, post_states, rewards, terminals = self._memory.minibatch(self._minibatch_size)
self._trainer.train_minibatch(
self._trainer.loss_function.argument_map(
pre_states=pre_states,
actions=Value.one_hot(actions.reshape(-1, 1).tolist(), self.nb_actions),
post_states=post_states,
rewards=rewards,
terminals=terminals
)
)
# Update the Target Network if needed
if (agent_step % self._target_update_interval) == 0:
self._target_net = self._action_value_net.clone(CloneMethod.freeze)
filename = "models\model%d" % agent_step
self._trainer.save_checkpoint(filename)
def _plot_metrics(self):
"""Plot current buffers accumulated values to visualize agent learning
"""
if len(self._episode_q_means) > 0:
mean_q = np.asscalar(np.mean(self._episode_q_means))
self._metrics_writer.write_value('Mean Q per ep.', mean_q, self._num_actions_taken)
if len(self._episode_q_stddev) > 0:
std_q = np.asscalar(np.mean(self._episode_q_stddev))
self._metrics_writer.write_value('Mean Std Q per ep.', std_q, self._num_actions_taken)
self._metrics_writer.write_value('Sum rewards per ep.', sum(self._episode_rewards), self._num_actions_taken)
def __init__(self, input_shape, nb_actions,
gamma=0.99, explorer=LinearEpsilonAnnealingExplorer(1, 0.1, 1000000),
learning_rate=0.00025, momentum=0.95, minibatch_size=32,
memory_size=500000, train_after=10000, train_interval=4, target_update_interval=10000,
monitor=True):
self.input_shape = input_shape
self.nb_actions = nb_actions
self.gamma = gamma
self._train_after = train_after
self._train_interval = train_interval
self._target_update_interval = target_update_interval
self._explorer = explorer
self._minibatch_size = minibatch_size
self._history = History(input_shape)
self._memory = ReplayMemory(memory_size, input_shape[1:], 4)
self._num_actions_taken = 0
# Metrics accumulator
self._episode_rewards, self._episode_q_means, self._episode_q_stddev = [], [], []
# Action Value model (used by agent to interact with the environment)
with default_options(activation=relu, init=he_uniform()):
self._action_value_net = Sequential([
Convolution2D((8, 8), 16, strides=4),
Convolution2D((4, 4), 32, strides=2),
Convolution2D((3, 3), 32, strides=1),
Dense(256, init=he_uniform(scale=0.01)),
Dense(nb_actions, activation=None, init=he_uniform(scale=0.01))
])
self._action_value_net.update_signature(Tensor[input_shape])
# Target model used to compute the target Q-values in training, updated
# less frequently for increased stability.
self._target_net = self._action_value_net.clone(CloneMethod.freeze)
# Function computing Q-values targets as part of the computation graph
@Function
@Signature(post_states=Tensor[input_shape], rewards=Tensor[()], terminals=Tensor[()])
def compute_q_targets(post_states, rewards, terminals):
return element_select(
terminals,
rewards,
gamma * reduce_max(self._target_net(post_states), axis=0) + rewards,
)
# Define the loss, using Huber Loss (more robust to outliers)
@Function
@Signature(pre_states=Tensor[input_shape], actions=Tensor[nb_actions],
post_states=Tensor[input_shape], rewards=Tensor[()], terminals=Tensor[()])
def criterion(pre_states, actions, post_states, rewards, terminals):
# Compute the q_targets
q_targets = compute_q_targets(post_states, rewards, terminals)
# actions is a 1-hot encoding of the action done by the agent
q_acted = reduce_sum(self._action_value_net(pre_states) * actions, axis=0)
# Define training criterion as the Huber Loss function
return huber_loss(q_targets, q_acted, 1.0)
# Adam based SGD
lr_schedule = learning_rate_schedule(learning_rate, UnitType.minibatch)
m_schedule = momentum_schedule(momentum)
vm_schedule = momentum_schedule(0.999)
l_sgd = adam(self._action_value_net.parameters, lr_schedule,
momentum=m_schedule, variance_momentum=vm_schedule)
self._metrics_writer = TensorBoardProgressWriter(freq=1, log_dir='metrics', model=criterion) if monitor else None
self._learner = l_sgd
self._trainer = Trainer(criterion, (criterion, None), l_sgd, self._metrics_writer)
