def multinput(th):
assert isinstance(th, Config)
model = Classifier(mark=th.mark)
# Add hidden layers
assert isinstance(th.fc_dims, list)
subnet = model.add(inter_type=model.CONCAT)
for dims in th.fc_dims:
subsubnet = subnet.add()
# Add input layer
subsubnet.add(Input(sample_shape=th.input_shape))
subsubnet.add(Flatten())
assert isinstance(dims, list)
for dim in dims:
subsubnet.add(Linear(output_dim=dim))
if core.use_bn: subsubnet.add(BatchNormalization())
subsubnet.add(Activation(th.actype1))
# Add output layer
model.add(Linear(output_dim=th.num_classes))
# Build model
optimizer = tf.train.AdamOptimizer(learning_rate=th.learning_rate)
model.build(optimizer=optimizer)
return model
def multinput(th):
assert isinstance(th, Config)
model = Classifier(mark=th.mark)
# Add hidden layers
assert isinstance(th.fc_dims, list)
subnet = model.add(inter_type=model.CONCAT)
for dims in th.fc_dims:
subsubnet = subnet.add()
# Add input layer
subsubnet.add(Input(sample_shape=th.input_shape))
subsubnet.add(Flatten())
assert isinstance(dims, list)
for dim in dims:
subsubnet.add(Linear(output_dim=dim))
# if cf10_core.use_bn: subsubnet.add(BatchNormalization())
subsubnet.add(Activation(th.actype1))
# Add output layer
model.add(Linear(output_dim=th.num_classes))
# Build model
model.build(metric=['accuracy', 'loss'],
batch_metric='accuracy',
eval_metric='accuracy')
return model
def typical(th, cell):
assert isinstance(th, Config)
# Initiate a model
model = Classifier(mark=th.mark, net_type=Recurrent)
# Add layers
model.add(Input(sample_shape=th.input_shape))
# Add hidden layers
model.add(cell)
# Build model and return
output_and_build(model, th)
return model
def unet(th):
assert isinstance(th, Config)
model = Classifier(mark=th.mark)
model.add(Input(sample_shape=th.input_shape))
def add_encoder_block(filters,
kernel_size=3,
add_pool=True,
drop_out=False):
model.add(Conv2D(filters, kernel_size))
model.add(Activation.ReLU())
model.add(Conv2D(filters, kernel_size))
output = model.add(Activation.ReLU())
if drop_out: output = model.add(Dropout(0.5))
if add_pool: model.add(MaxPool2D((2, 2), 2))
return output
def add_decoder_block(filters, convX, ks1=2, ks2=3):
model.add(Deconv2D(filters, ks1, strides=(2, 2)))
model.add(Activation.ReLU())
model.add(Concatenate(convX))
model.add(Conv2D(filters, ks2))
model.add(Activation.ReLU())
model.add(Conv2D(filters, ks2))
model.add(Activation.ReLU())
# Construct encoder part
conv64 = add_encoder_block(64)
conv128 = add_encoder_block(128)
conv256 = add_encoder_block(256)
conv512 = add_encoder_block(512, drop_out=True)
add_encoder_block(1024, add_pool=False, drop_out=True)
# Construct decoder part
add_decoder_block(512, conv512)
add_decoder_block(256, conv256)
add_decoder_block(128, conv128)
add_decoder_block(64, conv64)
# Add output layers
model.add(Conv2D(2, 3))
model.add(Activation.ReLU())
model.add(Conv2D(1, 1))
model.add(Activation('sigmoid'))
model.build(optimizer=tf.train.AdamOptimizer(th.learning_rate),
loss='binary_cross_entropy',
metric='accuracy')
return model
def fc_lstm(th):
assert isinstance(th, Config)
th.mark = 'fc_lstm_' + th.mark
# Initiate a model
model = Classifier(mark=th.mark, net_type=Recurrent)
# Add input layer
model.add(Input(sample_shape=th.input_shape))
# Add fc layers
for dim in th.fc_dims:
checker.check_positive_integer(dim)
model.add(Linear(output_dim=dim))
# model.add(BatchNorm())
model.add(Activation('relu'))
# Add lstm cells
for dim in th.rc_dims:
model.add(BasicLSTMCell(state_size=dim))
# Add output layer
# model.add(Linear(output_dim=th.output_dim))
# Build model
optimizer = tf.train.AdamOptimizer(th.learning_rate)
model.build(optimizer)
return model
def get_container(th, flatten=False): assert isinstance(th, Config) model = Classifier(mark=th.mark) model.add(Input(sample_shape=th.input_shape)) model.add(Normalize(sigma=255.)) if th.centralize_data: model.add(Normalize(mu=th.data_mean)) if flatten: model.add(Flatten()) return model
def get_container(th, flatten=False):
assert isinstance(th, Config)
model = Classifier(mark=th.mark)
model.add(Input(sample_shape=th.input_shape))
model.add(Normalize(sigma=255.))
if th.centralize_data: model.add(Normalize(mu=th.data_mean))
if flatten:
model.add(Flatten())
# Register extractor and researcher
model.register_extractor(mn_du.MNIST.connection_heat_map_extractor)
monitor.register_grad_researcher(mn_du.MNIST.flatten_researcher)
return model
def vanilla(mark):
model = Classifier(mark=mark)
model.add(Input(sample_shape=[784]))
def fc_bn_relu(bn=True):
model.add(Linear(100))
model.add(Activation('relu'))
if bn:
model.add(BatchNorm())
fc_bn_relu()
fc_bn_relu()
model.add(Linear(10))
# Build model
model.build(loss='cross_entropy',
optimizer=tf.train.GradientDescentOptimizer(0.01))
return model
def typical(th, cells):
assert isinstance(th, Config)
# Initiate a model
model = Classifier(mark=th.mark, net_type=Recurrent)
# Add layers
model.add(Input(sample_shape=th.input_shape, dtype=tf.int32))
model.add(Onehot(depth=th.num_classes))
emb_init = tf.initializers.random_uniform(-1, 1)
model.add(Dense(th.hidden_dim, use_bias=False,
weight_initializer=emb_init))
if th.input_dropout > 0: model.add(Dropout(1 - th.input_dropout))
# Add hidden layers
if not isinstance(cells, (list, tuple)): cells = [cells]
for cell in cells:
model.add(cell)
# Build model and return
output_and_build(model, th)
return model
def lstm_test(th):
assert isinstance(th, Config)
# Initiate model
th.mark = 'lstm_' + th.mark
model = Classifier(mark=th.mark, net_type=Recurrent)
# Add input layer
model.add(Input(sample_shape=[th.memory_depth]))
# Add hidden layers
for _ in range(th.num_blocks):
model.add(BasicLSTMCell(th.hidden_dim, with_peepholes=False))
# Add output layer
model.add(Linear(output_dim=41))
model.add(Activation('softmax'))
# Build model
optimizer = tf.train.AdamOptimizer(learning_rate=th.learning_rate)
model.build(optimizer=optimizer)
return model
def lstm0(th):
assert isinstance(th, Config)
th.mark = 'lstm_' + th.mark
# Initiate a model
model = Classifier(mark=th.mark, net_type=Recurrent)
# Add input layer
model.add(Input(sample_shape=th.input_shape))
# Add lstm cells
for dim in th.rc_dims:
model.add(BasicLSTMCell(state_size=dim))
# Add output layer
model.add(Linear(output_dim=th.output_dim))
# Build model
optimizer = tf.train.AdamOptimizer(th.learning_rate)
model.build(optimizer)
return model
def conv_2d_test(th):
assert isinstance(th, Config)
# Initiate model
th.mark = 'cnn_2d' + th.mark
def data_dim(sample_rate=44100, duration=2, n_mfcc=40):
audio_length = sample_rate * duration
dim = (n_mfcc, 1 + int(np.floor(audio_length / 512)), 1)
return dim
dim = data_dim()
model = Classifier(mark=th.mark)
# Add input layer
model.add(Input(sample_shape=[dim[0], dim[1], 1]))
# Add hidden layers
model.add(Conv2D(32, (4, 10), padding='same'))
model.add(BatchNorm())
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(Dropout(0.7))
model.add(Conv2D(32, (4, 10), padding='same'))
model.add(BatchNorm())
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(Dropout(0.7))
model.add(Conv2D(32, (4, 10), padding='same'))
model.add(BatchNorm())
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(Dropout(0.7))
model.add(Conv2D(32, (4, 10), padding='same'))
model.add(BatchNorm())
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(Dropout(0.7))
model.add(Flatten())
model.add(Linear(output_dim=64))
model.add(BatchNorm())
model.add(Activation('relu'))
# Add output layer
model.add(Linear(output_dim=41))
model.add(Activation('softmax'))
# Build model
optimizer = tf.train.AdamOptimizer(learning_rate=th.learning_rate)
model.build(optimizer=optimizer)
return model
def ka_convnet(mark):
model = Classifier(mark=mark)
model.add(Input(sample_shape=config.sample_shape))
strength = 1e-5
def ConvLayer(filters, bn=False):
model.add(
Conv2D(filters=filters,
kernel_size=5,
padding='same',
kernel_regularizer=regularizers.L2(strength=strength)))
if bn:
model.add(BatchNorm())
model.add(Activation.ReLU())
# Define structure
ConvLayer(32)
model.add(Dropout(0.5))
ConvLayer(32, False)
model.add(Dropout(0.5))
model.add(MaxPool2D(2, 2, 'same'))
ConvLayer(64, True)
model.add(Dropout(0.5))
model.add(MaxPool2D(2, 2, 'same'))
model.add(Flatten())
model.add(Linear(128))
model.add(Activation.ReLU())
# model.add(Dropout(0.5))
model.add(Linear(10))
# Build model
model.build(optimizer=tf.train.AdamOptimizer(learning_rate=1e-4))
return model
def deep_conv(mark):
# Initiate predictor
model = Classifier(mark=mark)
model.add(Input(sample_shape=config.sample_shape))
def ConvBNReLU(filters, strength=1.0, bn=True):
model.add(
Conv2D(filters=filters,
kernel_size=5,
padding='same',
kernel_regularizer=regularizers.L2(strength=strength)))
if bn:
model.add(BatchNorm())
model.add(Activation('relu'))
# Conv layers
reg = 1e-5
ConvBNReLU(32, reg)
model.add(Dropout(0.5))
ConvBNReLU(32, reg)
model.add(MaxPool2D(2, 2, 'same'))
ConvBNReLU(64, reg)
model.add(Dropout(0.5))
ConvBNReLU(64, reg)
model.add(MaxPool2D(2, 2, 'same'))
ConvBNReLU(128, reg)
# FC layers
model.add(Flatten())
model.add(Linear(256))
# model.add(BatchNorm())
model.add(Activation('relu'))
model.add(Linear(256))
# model.add(BatchNorm())
model.add(Activation('relu'))
model.add(Linear(config.y_dim))
# Build model
model.build(optimizer=tf.train.AdamOptimizer(learning_rate=1e-4))
return model
def multinput_mlp(th):
assert isinstance(th, Config)
model = Classifier(mark=th.mark)
def data_dim(sample_rate=16000, duration=2, n_mfcc=50):
audio_length = sample_rate * duration
dim = (n_mfcc, 1 + int(np.floor(audio_length / 512)), 1)
return dim
dim = data_dim()
# Add hidden layers
subnet = model.add(inter_type=model.CONCAT)
subsubnet = subnet.add()
subsubnet.add(Input(sample_shape=[32000, 1]))
subsubnet.add(Linear(output_dim=512))
subsubnet.add(Activation('relu'))
subsubnet.add(Linear(output_dim=256))
subsubnet.add(Activation('relu'))
subsubnet = subnet.add()
subsubnet.add(Input(sample_shape=[dim[0], dim[1], 1], name='mfcc'))
subsubnet.add(Conv2D(32, (4, 10), padding='same'))
subsubnet.add(BatchNorm())
subsubnet.add(Activation('relu'))
subsubnet.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
subsubnet.add(Dropout(0.8))
# subsubnet.add(Conv2D(32, (4, 10), padding='same'))
# subsubnet.add(BatchNorm())
# subsubnet.add(Activation('relu'))
# subsubnet.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
# subsubnet.add(Dropout(0.8))
subsubnet.add(Conv2D(32, (4, 10), padding='same'))
subsubnet.add(BatchNorm())
subsubnet.add(Activation('relu'))
subsubnet.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
subsubnet.add(Dropout(0.7))
subsubnet.add(Flatten())
model.add(Linear(output_dim=128))
model.add(BatchNorm())
model.add(Activation('relu'))
model.add(Linear(output_dim=64))
model.add(BatchNorm())
model.add(Activation('relu'))
model.add(Linear(output_dim=64))
model.add(BatchNorm())
model.add(Activation('relu'))
# Add output layer
model.add(Linear(output_dim=41))
model.add(Activation('softmax'))
# Build model
optimizer = tf.train.AdamOptimizer(learning_rate=th.learning_rate)
model.build(optimizer=optimizer)
return model
def res_00(th):
assert isinstance(th, Config)
model = Classifier(mark=th.mark)
def data_dim(sample_rate=16000, duration=2, n_mfcc=50):
audio_length = sample_rate * duration
dim = (n_mfcc, 1 + int(np.floor(audio_length / 512)), 1)
return dim
dim = data_dim()
# Add hidden layers
subnet = model.add(inter_type=model.CONCAT)
# the net to process raw data
subsubnet = subnet.add()
# subsubnet.add(Input(sample_shape=[32000, 1], name='raw_data'))
subsubnet.add(Input(sample_shape=[32000, 1]))
subsubnet.add(Conv1D(filters=16, kernel_size=9, padding='valid'))
subsubnet.add(BatchNorm())
subsubnet.add(Activation('relu'))
subsubnet.add(Conv1D(filters=16, kernel_size=9, padding='valid'))
subsubnet.add(BatchNorm())
subsubnet.add(Activation('relu'))
subsubnet.add(MaxPool1D(pool_size=16, strides=16))
subsubnet.add(Dropout(th.raw_keep_prob))
subsubnet.add(Conv1D(filters=32, kernel_size=3, padding='valid'))
subsubnet.add(Activation('relu'))
subsubnet.add(Conv1D(filters=32, kernel_size=3, padding='valid'))
subsubnet.add(Activation('relu'))
subsubnet.add(MaxPool1D(pool_size=4, strides=4))
subsubnet.add(Dropout(th.raw_keep_prob))
subsubnet.add(Conv1D(filters=32, kernel_size=3, padding='valid'))
subsubnet.add(Activation('relu'))
subsubnet.add(Conv1D(filters=32, kernel_size=3, padding='valid'))
subsubnet.add(Activation('relu'))
subsubnet.add(MaxPool1D(pool_size=4, strides=4))
subsubnet.add(Conv1D(filters=256, kernel_size=3, padding='valid'))
subsubnet.add(BatchNorm())
subsubnet.add(Activation('relu'))
subsubnet.add(Conv1D(filters=256, kernel_size=3, padding='valid'))
subsubnet.add(BatchNorm())
subsubnet.add(Activation('relu'))
subsubnet.add(GlobalMaxPooling1D())
# the net to process mfcc features
subsubnet = subnet.add()
subsubnet.add(Input(sample_shape=[dim[0], dim[1], 1], name='mfcc'))
subsubnet.add(Conv2D(32, (4, 10), padding='same'))
subsubnet.add(BatchNorm())
subsubnet.add(Activation('relu'))
subsubnet.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
subsubnet.add(Dropout(th.mfcc_keep_prob))
net = subsubnet.add(ResidualNet())
net.add(Conv2D(32, (4, 10), padding='same'))
net.add(BatchNorm())
net.add_shortcut()
subsubnet.add(Activation('relu'))
subsubnet.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
subsubnet.add(Dropout(th.mfcc_keep_prob))
#
net = subsubnet.add(ResidualNet())
net.add(Conv2D(32, (4, 10), padding='same'))
net.add(BatchNorm())
net.add_shortcut()
subsubnet.add(Activation('relu'))
subsubnet.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
subsubnet.add(Dropout(th.mfcc_keep_prob))
net = subsubnet.add(ResidualNet())
net.add(Conv2D(32, (4, 10), padding='same'))
net.add(BatchNorm())
net.add_shortcut()
subsubnet.add(Activation('relu'))
subsubnet.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
subsubnet.add(Dropout(th.mfcc_keep_prob))
net = subsubnet.add(ResidualNet())
net.add(Conv2D(32, (4, 10), padding='same'))
net.add(BatchNorm())
net.add_shortcut()
subsubnet.add(Activation('relu'))
subsubnet.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
subsubnet.add(Dropout(th.mfcc_keep_prob))
subsubnet.add(Flatten())
subsubnet.add(Dropout(th.concat_keep_prob))
model.add(Linear(output_dim=128))
model.add(BatchNorm())
model.add(Activation('relu'))
#
model.add(Linear(output_dim=64))
model.add(BatchNorm())
model.add(Activation('relu'))
# Add output layer
model.add(Linear(output_dim=41))
model.add(Activation('softmax'))
# Build model
optimizer = tf.train.AdamOptimizer(learning_rate=th.learning_rate)
model.build(optimizer=optimizer)
return model
def conv_test(th):
assert isinstance(th, Config)
# Initiate model
th.mark = 'cnn' + th.mark
model = Classifier(mark=th.mark)
# Add input layer
model.add(Input(sample_shape=[32000, 1]))
# Add hidden layers
model.add(Conv1D(filters=16, kernel_size=9, padding='valid'))
model.add(Activation('relu'))
model.add(Conv1D(filters=16, kernel_size=9, padding='valid'))
model.add(Activation('relu'))
model.add(MaxPool1D(pool_size=16, strides=16))
# model.add(Dropout(0.9))
model.add(Conv1D(filters=32, kernel_size=3, padding='valid'))
model.add(Activation('relu'))
model.add(Conv1D(filters=32, kernel_size=3, padding='valid'))
model.add(Activation('relu'))
model.add(MaxPool1D(pool_size=4, strides=4))
# model.add(Dropout(0.9))
model.add(Conv1D(filters=32, kernel_size=3, padding='valid'))
model.add(Activation('relu'))
model.add(Conv1D(filters=32, kernel_size=3, padding='valid'))
model.add(Activation('relu'))
model.add(MaxPool1D(pool_size=4, strides=4))
# model.add(Dropout(0.9))
#
model.add(Conv1D(filters=256, kernel_size=3, padding='valid'))
model.add(Activation('relu'))
model.add(Conv1D(filters=256, kernel_size=3, padding='valid'))
model.add(Activation('relu'))
model.add(GlobalMaxPooling1D())
# model.add(Dropout(0.8))
#
model.add(Linear(output_dim=64))
model.add(Activation('relu'))
model.add(Linear(output_dim=1028))
model.add(Activation('relu'))
# Add output layer
model.add(Linear(output_dim=41))
model.add(Activation('softmax'))
# Build model
optimizer = tf.train.AdamOptimizer(learning_rate=th.learning_rate)
model.build(optimizer=optimizer)
return model
def mlp(th):
assert isinstance(th, Config)
# Initiate a model
model = Classifier(mark=th.mark)
# Add input layer
model.add(Input(sample_shape=th.input_shape))
model.add(Flatten())
# Add hidden layers
assert isinstance(th.fc_dims, list)
for dim in th.fc_dims:
model.add(Linear(output_dim=dim))
model.add(BatchNormalization())
model.add(Activation(th.actype1))
# Add output layer
model.add(Linear(output_dim=th.num_classes))
# Build model
optimizer = tf.train.AdamOptimizer(learning_rate=th.learning_rate)
model.build(optimizer=optimizer)
return model
def mlp(th):
assert isinstance(th, Config)
# Initiate a model
model = Classifier(mark=th.mark)
# Add input layer
model.add(Input([32000]))
# Add hidden layers
for _ in range(th.num_blocks):
model.add(Linear(output_dim=th.hidden_dim))
model.add(BatchNorm())
# model.add(BatchNormalization())
model.add(Activation(th.actype1))
# model.add(Dropout(0.9))
# Add output layer
model.add(Linear(output_dim=th.num_classes))
model.add(Activation('softmax'))
# Build model
optimizer = tf.train.AdamOptimizer(learning_rate=th.learning_rate)
model.build(optimizer=optimizer)
return model