def build(self, hp):
maxnorm = hp.Choice('maxnorm', values=self.hyperparam['maxnorm'])
resnet_model = Resnet(input_shape=(self.seqlen, self.channels), norm_max=maxnorm)
if self.pretrained_weights is not None:
resnet_model.set_weights(self.pretrained_weights)
inp = Input(shape=(self.seqlen, self.channels))
enc_inp = resnet_model(inp)
dense_units = hp.Int('preclassification', min_value = self.hyperparam['dense_units']['min'],\
max_value = self.hyperparam['dense_units']['max'], step = self.hyperparam['dense_units']['step'])
dense_out = Dense(units = dense_units, activation='relu',
kernel_constraint=MaxNorm(maxnorm,axis=[0,1]),
bias_constraint=MaxNorm(maxnorm,axis=0),
kernel_initializer=glorot_uniform(seed=0))(enc_inp)
dense_out = Dropout(rate=hp.Choice('dropout', values = self.hyperparam['dropout']))(dense_out)
output = Dense(self.num_classes, activation='softmax',
kernel_constraint=MaxNorm(maxnorm,axis=[0,1]),
bias_constraint=MaxNorm(maxnorm,axis=0),
kernel_initializer=glorot_uniform(seed=0))(dense_out)
model = Model(inputs=inp, outputs=output)
model.compile(optimizer=Adam(lr=hp.Choice('lr', values = self.hyperparam['lr'])),
loss=focal_loss(), metrics=['accuracy', macro_f1])
return model
def main(argv):
infile = argv[0]
outfile = argv[1]
seqlen = 1500
channels = 6
maxnorm = 20.0
# Create model
resnet_model = Resnet(input_shape=(seqlen, channels), norm_max=maxnorm)
samp1 = Input(shape=(seqlen, channels))
enc_samp1 = resnet_model(samp1)
samp2 = Input(shape=(seqlen, channels))
enc_samp2 = resnet_model(samp2)
diff_layer = Lambda(lambda tensors: K.abs(tensors[0] - tensors[1]))
diff_enc = diff_layer([enc_samp1, enc_samp2])
dense_out = Dense(50,
activation='relu',
kernel_constraint=MaxNorm(maxnorm, axis=[0, 1]),
bias_constraint=MaxNorm(maxnorm, axis=0),
kernel_initializer=glorot_uniform(seed=0))(diff_enc)
dense_out = Dropout(rate=0.2)(dense_out)
output = Dense(1,
activation='sigmoid',
kernel_constraint=MaxNorm(maxnorm, axis=[0, 1]),
bias_constraint=MaxNorm(maxnorm, axis=0),
kernel_initializer=glorot_uniform(seed=0))(dense_out)
model = Model(inputs=[samp1, samp2], outputs=output)
model.load_weights(infile)
for layer in model.layers:
if layer.name == "model":
resnet_model.set_weights(layer.get_weights())
resnet_model.save_weights(outfile)
def create_model(beta_1=0.8,
beta_2=0.999,
learning_rate=0.01,
drop_rate_input=0.2,
drop_rate_hidden=0.3,
weight_constraint=3,
units=80,
seed=config.SEED):
model = Sequential()
model.add(Dropout(drop_rate_input, seed=seed))
model.add(
Dense(units=units,
activation='relu',
kernel_constraint=MaxNorm(max_value=weight_constraint, axis=0)))
model.add(Dropout(drop_rate_hidden, seed=seed))
model.add(
Dense(units=units / 2,
activation='relu',
kernel_constraint=MaxNorm(max_value=weight_constraint, axis=0)))
model.add(Dropout(drop_rate_hidden, seed=seed))
model.add(Dense(units=1, activation='sigmoid'))
# For a binary classification problem
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate,
beta_1=beta_1,
beta_2=beta_2,
epsilon=1e-07,
amsgrad=False,
name='Adam')
model.compile(loss='binary_crossentropy',
metrics=["accuracy"],
optimizer=optimizer)
return model
def mf(n_person=100, n_item=3000, para_dim=5):
"""
Input: dimensions of person-item matrix
Output: neural network with multiple inputs and embedding layers
"""
p = Input(shape=[1], name='person')
p_e = Embedding(n_person,
para_dim,
embeddings_initializer='RandomNormal',
name='person_embedding')(p)
p_e = MaxNorm(max_value=5 * np.sqrt(para_dim))(p_e) # maxnorm
i = Input(shape=[1], name='item')
i_e = Embedding(n_item,
para_dim,
embeddings_initializer='RandomNormal',
name='item_embedding')(i)
i_e = MaxNorm(max_value=5 * np.sqrt(para_dim))(i_e) # maxnorm
d = Input(shape=[1], name='residual')
d_e = Embedding(n_item,
1,
embeddings_initializer='RandomNormal',
name='res_embed')(i)
d_e = MaxNorm(max_value=5 * np.sqrt(para_dim))(d_e) # maxnorm
output = Dot(axes=-1, name='dotProduct')([p_e, i_e]) + d_e
# print(output.shape)
output = Flatten(name='output')(output)
main_output = Activation('sigmoid')(output)
# print(main_output.shape)
model = Model([p, i, d], main_output)
return model
def identity_block(inputs, filters, ksz, stage, block, norm_max=1.0):
"""
Identity block for ResNet
Parameters
__________
inputs : input tensor of shape (batch_size, num_steps_prev, num_ch_prev)
filters : list of 3 integers defining num of conv filters in main path
ksz : filter width of middle convolutional layer
stage : integer used to name layers
block : string used to name layers
norm_max : maximum norm for constraint
Returns
_______
x - output of the identity block, tensor of shape (batch_size, num_steps, num_ch)
"""
conv_base_name = 'res' + str(stage) + block + '_branch'
bn_base_name = 'bn' + str(stage) + block + '_branch'
F1, F2, F3 = filters
# Shortcut path
x_shortcut = inputs
# Main path
x = Conv1D(filters=F1, kernel_size=1, strides=1, padding='valid',
name=conv_base_name + '2a', use_bias=False,
kernel_constraint=MaxNorm(norm_max,axis=[0,1,2]),
kernel_initializer=glorot_uniform(seed=0))(inputs)
x = LeakyReLU(alpha=0.1)(x)
x = BatchNormalization(axis=-1, momentum=0.9, name=bn_base_name + '2a',
gamma_constraint=MaxNorm(norm_max,axis=0),
beta_constraint=MaxNorm(norm_max,axis=0))(x)
x = Conv1D(filters=F2, kernel_size=ksz, strides=1, padding='same',
name=conv_base_name + '2b', use_bias=False,
kernel_constraint=MaxNorm(norm_max,axis=[0,1,2]),
kernel_initializer=glorot_uniform(seed=0))(x)
x = LeakyReLU(alpha=0.1)(x)
x = BatchNormalization(axis=-1, momentum=0.9, name=bn_base_name + '2b',
gamma_constraint=MaxNorm(norm_max,axis=0),
beta_constraint=MaxNorm(norm_max,axis=0))(x)
x = Conv1D(filters=F3, kernel_size=1, strides=1, padding='valid',
name=conv_base_name + '2c', use_bias=False,
kernel_constraint=MaxNorm(norm_max,axis=[0,1,2]),
kernel_initializer=glorot_uniform(seed=0))(x)
x = Add()([x, x_shortcut])
x = LeakyReLU(alpha=0.1)(x)
x = BatchNormalization(axis=-1, momentum=0.9, name=bn_base_name + '2c',
gamma_constraint=MaxNorm(norm_max,axis=0),
beta_constraint=MaxNorm(norm_max,axis=0))(x)
return x
def wrap_model():
model = Sequential()
# add first embedding layer with pretrained wikipedia weights
model.add(
Embedding(
embedding_matrix.shape[0],
embedding_matrix.shape[1],
weights=[embedding_matrix],
input_length=max_sequence_length,
trainable=False
)
)
# add LSTM layer
model.add(
LSTM(
lstm_out_width,
input_shape=(max_sequence_length, ),
go_backwards=backwards,
dropout=dropout,
recurrent_dropout=dropout,
return_sequences=True,
kernel_constraint=MaxNorm(),
)
)
model.add(
MaxPooling1D(
pool_size=lstm_pool_size,
)
)
model.add(
Flatten()
)
# Add output layer
model.add(
Dense(
1,
activation='sigmoid'
)
)
if optimizer == "rmsprop":
optimizer_fn = RMSprop(lr=0.01*learn_rate_mult)
else:
optimizer_fn = Adam(lr=0.1*learn_rate_mult)
# Compile model
model.compile(
loss='binary_crossentropy', optimizer=optimizer_fn,
metrics=['acc'])
if verbose == 1:
model.summary()
return model
def train():
# load dataset
(train_X, train_Y), (test_X, test_Y) = cifar10.load_data()
train_x = train_X.astype('float32')
test_X = test_X.astype('float32')
train_X = train_X/255.0
test_X = test_X/255.0
train_Y = to_categorical(train_Y)
test_Y = to_categorical(test_Y)
num_classes = test_Y.shape[1]
# model
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(32, 32, 3),
padding='same', activation='relu',
kernel_constraint=MaxNorm(3)))
model.add(Dropout(0.2))
model.add(Conv2D(32, (3, 3), activation='relu',
padding='same', kernel_constraint=MaxNorm(3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_constraint=MaxNorm(3)))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
sgd = SGD(lr=0.01, momentum=0.9, decay=(0.01/25))
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
model.fit(train_X, train_Y,
validation_data=(test_X, test_Y),
epochs=15, batch_size=32)
_, acc = model.evaluate(test_X, test_Y)
print(acc*100)
model.save("model1_cifar_10epoch.h5")
def create_resnet_model(seqlen, num_channels, maxnorm, modeldir, dense_units,
num_classes):
resnet_model = Resnet(input_shape=(seqlen, num_channels), norm_max=maxnorm)
# Load weights from pretrained model
resnet_model.load_weights(os.path.join(modeldir, 'pretrained_resnet.h5'))
samp = Input(shape=(seqlen, num_channels))
enc_samp = resnet_model(samp)
dense_out = Dense(dense_units,
activation='relu',
kernel_constraint=MaxNorm(maxnorm, axis=[0, 1]),
bias_constraint=MaxNorm(maxnorm, axis=0),
kernel_initializer=glorot_uniform(seed=0),
name='FC1')(enc_samp)
dense_out = Dropout(rate=0.2)(dense_out)
output = Dense(num_classes,
activation='softmax',
kernel_constraint=MaxNorm(maxnorm, axis=[0, 1]),
bias_constraint=MaxNorm(maxnorm, axis=0),
kernel_initializer=glorot_uniform(seed=0),
name='output')(dense_out)
model = Model(inputs=samp, outputs=output)
return model
def analyze_train(train_csv_filename, test_csv_filename):
train_dataset = np.loadtxt(train_csv_filename, delimiter=',')
test_dataset = np.loadtxt(test_csv_filename, delimiter=',')
# split into input (X) and output (Y) variables
x_train = train_dataset[:, 2:]
y_train = train_dataset[:, 1]
x_test = test_dataset[:, 2:]
y_test = test_dataset[:, 1]
model = Sequential()
# BEST RESULTS
model.add(Dense(16, input_dim=36, activation='relu', kernel_constraint=MaxNorm(3)))
model.add(Dropout(0.3))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# es_callback = EarlyStopping(monitor='val_loss', patience=5)
# history = model.fit(x_train, y_train, epochs=75, batch_size=128, validation_split=0.2, verbose=1, callbacks=[es_callback])
history = model.fit(x_train, y_train, epochs=250, batch_size=32, validation_split=0.2, verbose=1)
print(model.evaluate(x_test, y_test))
# graph training vs. validation accuracy over epochs
plt.figure(1)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
# graph training vs. validation loss over epochs
plt.figure(2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
return model
def Conv1DTranspose(inputs, filters, ksz, s=2, padding='same', norm_max=1.0):
"""
1D Transposed convolution for FCN
Parameters
__________
inputs : input tensor of shape (batch_size, num_steps_prev, num_ch_prev)
filters : integer defining num of conv filters
ksz : filter width of convolutional layer
s : integer specifying stride
padding : padding for the convolutional layer
norm_max : maximum norm for constraint
Returns
_______
x : output tensor of shape (batch_size, num_steps, num_ch)
"""
x = Lambda(lambda x: K.expand_dims(x, axis=2))(inputs)
x = Conv2DTranspose(filters=filters, kernel_size=(ksz, 1), strides=(s, 1), padding=padding,
name='conv_transpose', use_bias=False,
kernel_constraint=MaxNorm(norm_max,axis=[0,1,2,3]),
kernel_initializer=glorot_uniform(seed=0))(x)
x = Lambda(lambda x: K.squeeze(x, axis=2))(x)
return x
# print(y_train.shape)
# print(y_test.shape)
# Defining the model
input_shape = img_data[0].shape
print(input_shape)
model = Sequential()
model = Sequential()
model.add(
Conv2D(32, (3, 3),
input_shape=input_shape,
padding='same',
activation='relu',
kernel_constraint=MaxNorm(3)))
model.add(Dropout(0.2))
model.add(
Conv2D(32, (3, 3),
activation='relu',
padding='same',
kernel_constraint=MaxNorm(3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_constraint=MaxNorm(3)))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
epochs = 25
lrate = 0.01
def main(argv):
indir = args.indir
outdir = args.outdir
if not os.path.exists(outdir):
os.makedirs(outdir)
dirstr = 'lr{:.4f}-maxnorm{:.2f}-batchsize{:d}'.format(
args.lr, args.maxnorm, args.batchsize)
resultdir = os.path.join(outdir, dirstr)
if not os.path.exists(resultdir):
os.makedirs(resultdir)
# Hyperparameters
lr = args.lr # learning rate
num_epochs = args.num_epochs
batch_size = args.batchsize
# Read train data
ftrain = h5py.File(os.path.join(args.indir, 'train_dataset.h5'), 'r')
train_samples1 = ftrain['samp1']
train_samples2 = ftrain['samp2']
train_labels = np.array(ftrain['label'], dtype=np.int32)
[num_train, seqlen, channels] = train_samples1.shape
# Read validation data
fval = h5py.File(os.path.join(args.indir, 'val_dataset.h5'), 'r')
val_samples1 = fval['samp1']
val_samples2 = fval['samp2']
val_labels = np.array(fval['label'], dtype=np.int32)
[num_val, seqlen, channels] = val_samples1.shape
# Read test data
ftest = h5py.File(os.path.join(args.indir, 'test_dataset.h5'), 'r')
test_samples1 = ftest['samp1']
test_samples2 = ftest['samp2']
test_labels = np.array(ftest['label'], dtype=np.int32)
[num_test, seqlen, channels] = test_samples1.shape
# Data generators for train/val/test
train_gen = DataGenerator(train_samples1, train_samples2, train_labels,\
batch_size=batch_size, seqlen=seqlen, channels=channels,\
shuffle=True, balance=True, augment=False, aug_factor=0.25)
val_gen = DataGenerator(val_samples1, val_samples2, val_labels,\
batch_size=batch_size, seqlen=seqlen, channels=channels)
test_gen = DataGenerator(test_samples1, test_samples2, test_labels,\
batch_size=batch_size, seqlen=seqlen, channels=channels)
# Create model
resnet_model = Resnet(input_shape=(seqlen, channels),
norm_max=args.maxnorm)
samp1 = Input(shape=(seqlen, channels))
enc_samp1 = resnet_model(samp1)
samp2 = Input(shape=(seqlen, channels))
enc_samp2 = resnet_model(samp2)
diff_layer = Lambda(lambda tensors: K.abs(tensors[0] - tensors[1]))
diff_enc = diff_layer([enc_samp1, enc_samp2])
dense_out = Dense(50,
activation='relu',
kernel_constraint=MaxNorm(args.maxnorm, axis=[0, 1]),
bias_constraint=MaxNorm(args.maxnorm, axis=0),
kernel_initializer=glorot_uniform(seed=0))(diff_enc)
dense_out = Dropout(rate=0.2)(dense_out)
output = Dense(1,
activation='sigmoid',
kernel_constraint=MaxNorm(args.maxnorm, axis=[0, 1]),
bias_constraint=MaxNorm(args.maxnorm, axis=0),
kernel_initializer=glorot_uniform(seed=0))(dense_out)
model = Model(inputs=[samp1, samp2], outputs=output)
model.compile(optimizer=Adam(lr=lr),
loss=BinaryCrossentropy(),
metrics=['accuracy'])
# Train model
# Use early stopping and model checkpoints to handle overfitting and save best model
model_checkpt = ModelCheckpoint(os.path.join(resultdir,'{epoch:02d}-{val_accuracy:.4f}.h5'),\
monitor='val_accuracy')#,\
#mode='max', save_best_only=True)
batch_renorm_cb = BatchRenormScheduler(
len(train_gen)) # Implement batchrenorm after 1st epoch
history = model.fit(train_gen,
epochs=num_epochs,
validation_data=val_gen,
verbose=1,
shuffle=False,
callbacks=[batch_renorm_cb, model_checkpt],
workers=2,
max_queue_size=20,
use_multiprocessing=False)
# Plot training history
plot_results(history.history['loss'], history.history['val_loss'],\
os.path.join(resultdir,'loss.jpg'), metric='Loss')
plot_results(history.history['accuracy'], history.history['val_accuracy'],\
os.path.join(resultdir,'accuracy.jpg'), metric='Accuracy')
# Predict probability on validation data using best model
best_model_file, epoch, val_accuracy = get_best_model(resultdir)
print(
'Predicting with model saved at Epoch={:d} with val_accuracy={:0.4f}'.
format(epoch, val_accuracy))
model.load_weights(os.path.join(resultdir, best_model_file))
probs = model.predict(test_gen)
y_pred = probs.argmax(axis=1)
y_true = test_labels
test_acc = accuracy_score(y_true, y_pred)
print('Test accuracy = {:0.2f}'.format(test_acc * 100.0))
def unet(data_shape=(None, None, None),
channels_in=1,
channels_out=1,
starting_filter_number=32,
kernel_size=(3, 3, 3),
num_conv_per_pool=2,
num_repeat_bottom_conv=0,
pool_number=4,
pool_size=(2, 2, 2),
expansion_rate=2,
dropout_type='standard',
dropout_rate=0.25,
dropout_power=1 / 4,
dropblock_size=5,
add_conv_layers=4,
add_conv_filter_number=32,
add_conv_dropout_rate=None,
final_activation='linear',
gn_type='groups',
gn_param=32,
weight_constraint=None):
if len(data_shape) == 1:
print('using 1D operations')
Conv = Conv1D
UpSampling = UpSampling1D
MaxPool = MaxPool1D
if dropout_type == 'spatial':
DRPT = SpatialDropout1D
print('using spatial dropout')
elif (dropout_type == 'dropblock') or (dropout_type == 'block'):
DRPT = DB1D(block_size=dropblock_size)
print('using dropblock with blocksize:', dropblock_size)
else:
DRPT = Dropout
print('using standard dropout')
elif len(data_shape) == 2:
print('using 2D operations')
Conv = Conv2D
UpSampling = UpSampling2D
MaxPool = MaxPool2D
if dropout_type == 'spatial':
DRPT = SpatialDropout2D
print('using spatial dropout')
elif (dropout_type == 'dropblock') or (dropout_type == 'block'):
DRPT = DB2D(block_size=dropblock_size)
print('using dropblock with blocksize:', dropblock_size)
else:
DRPT = Dropout
print('using standard dropout')
elif len(data_shape) == 3:
print('using 3D operations')
Conv = Conv3D
UpSampling = UpSampling3D
MaxPool = MaxPool3D
if dropout_type == 'spatial':
DRPT = SpatialDropout3D
print('using spatial dropout')
elif (dropout_type == 'dropblock') or (dropout_type == 'block'):
DRPT = DB3D(block_size=dropblock_size)
print('using dropblock with blocksize:', dropblock_size)
else:
DRPT = Dropout
print('using standard dropout')
else:
print('Error: data_shape not compatible')
return None
if (weight_constraint == 'ws') or (weight_constraint
== 'weightstandardization'):
print('using weight standardization')
wsconstraint = WeightStandardization(mean=0, std=1)
elif (weight_constraint == 'maxnorm') or (weight_constraint == 'MaxNorm'):
print('using MaxNorm')
wsconstraint = MaxNorm(max_value=1,
axis=[ii for ii in range(len(data_shape) + 1)])
elif (weight_constraint == 'unitnorm') or (weight_constraint
== 'UnitNorm'):
print('using UnitNorm')
wsconstraint = UnitNorm(axis=[0, 1, 2])
else:
print('excluding weight constraints')
wsconstraint = None
layer_conv = {}
layer_nonconv = {}
number_of_layers_half = pool_number + 1
number_of_filters_max = np.round(
(expansion_rate**(number_of_layers_half - 1)) * starting_filter_number)
#first half of U
layer_nonconv[0] = Input(data_shape + (channels_in, ))
print()
print('Input:', layer_nonconv[0].shape)
print()
for layer_number in range(1, number_of_layers_half):
number_of_filters_current = np.round(
(expansion_rate**(layer_number - 1)) * starting_filter_number)
drop_rate_layer = dropout_rate * (np.power(
(number_of_filters_current / number_of_filters_max),
dropout_power))
if isinstance(pool_size, (list, )):
poolsize = pool_size[layer_number - 1]
else:
poolsize = pool_size
if isinstance(kernel_size, (list, )):
kernelsize = kernel_size[layer_number - 1]
else:
kernelsize = kernel_size
if gn_type == 'channels':
groups = int(
np.clip(number_of_filters_current / gn_param, 1,
number_of_filters_current))
else:
groups = int(np.clip(gn_param, 1, number_of_filters_current))
layer_conv[layer_number] = DRPT(rate=drop_rate_layer)(
GroupNormalization(groups=groups)(Conv(
filters=number_of_filters_current,
kernel_size=kernelsize,
padding='same',
activation='relu',
kernel_constraint=wsconstraint)(layer_nonconv[layer_number -
1])))
for _ in range(1, num_conv_per_pool):
layer_conv[layer_number] = DRPT(rate=drop_rate_layer)(
GroupNormalization(groups=groups)(Conv(
filters=number_of_filters_current,
kernel_size=kernelsize,
padding='same',
activation='relu',
kernel_constraint=wsconstraint)(layer_conv[layer_number])))
print('{:<30}'.format(str(layer_conv[layer_number].shape)),
'\tgroups:', groups, '\tkernel:', kernelsize, '\tdroprate:',
drop_rate_layer)
layer_nonconv[layer_number] = Concatenate(axis=-1)([
MaxPool(pool_size=poolsize)(layer_conv[layer_number]),
DRPT(rate=drop_rate_layer)(GroupNormalization(groups=groups)(Conv(
filters=number_of_filters_current,
kernel_size=poolsize,
strides=poolsize,
padding='valid',
activation='relu',
kernel_constraint=wsconstraint)(layer_conv[layer_number])))
])
#center of U
if isinstance(kernel_size, (list, )):
kernelsize = kernel_size[number_of_layers_half - 1]
else:
kernelsize = kernel_size
if gn_type == 'channels':
groups = int(
np.clip(
np.round((expansion_rate**(number_of_layers_half - 1)) *
starting_filter_number) / gn_param, 1,
np.round((expansion_rate**(number_of_layers_half - 1)) *
starting_filter_number)))
else:
groups = int(
np.clip(
gn_param, 1,
np.round((expansion_rate**(number_of_layers_half - 1)) *
starting_filter_number)))
layer_conv[number_of_layers_half] = DRPT(rate=dropout_rate)(
GroupNormalization(groups=groups)(Conv(
filters=np.round((expansion_rate**(number_of_layers_half - 1)) *
starting_filter_number),
kernel_size=kernelsize,
padding='same',
activation='relu',
kernel_constraint=wsconstraint)(
layer_nonconv[number_of_layers_half - 1])))
for _ in range(1, (num_repeat_bottom_conv + 1) * num_conv_per_pool):
layer_conv[number_of_layers_half] = DRPT(rate=dropout_rate)(
GroupNormalization(groups=groups)(Conv(
filters=np.round(
(expansion_rate**(number_of_layers_half - 1)) *
starting_filter_number),
kernel_size=kernelsize,
padding='same',
activation='relu',
kernel_constraint=wsconstraint)(
layer_conv[number_of_layers_half])))
print('{:<30}'.format(str(layer_conv[number_of_layers_half].shape)),
'\tgroups:', groups, '\tkernel:', kernelsize, '\tdroprate:',
dropout_rate)
#second half of U
for layer_number in range(number_of_layers_half + 1,
2 * number_of_layers_half):
number_of_filters_current = np.round(
(expansion_rate**(2 * number_of_layers_half - layer_number - 1)) *
starting_filter_number)
drop_rate_layer = dropout_rate * (np.power(
(number_of_filters_current / number_of_filters_max),
dropout_power))
if isinstance(pool_size, (list, )):
poolsize = pool_size[2 * number_of_layers_half - layer_number - 1]
else:
poolsize = pool_size
if isinstance(kernel_size, (list, )):
kernelsize = kernel_size[2 * number_of_layers_half - layer_number -
1]
else:
kernelsize = kernel_size
if gn_type == 'channels':
groups = int(
np.clip(number_of_filters_current / gn_param, 1,
number_of_filters_current))
else:
groups = int(np.clip(gn_param, 1, number_of_filters_current))
layer_nonconv[layer_number] = Concatenate(axis=-1)([
DRPT(rate=drop_rate_layer)(GroupNormalization(groups=groups)(Conv(
filters=number_of_filters_current,
kernel_size=kernelsize,
padding='same',
activation='relu',
kernel_constraint=wsconstraint)(UpSampling(size=poolsize)(
layer_conv[layer_number - 1])))),
layer_conv[2 * number_of_layers_half - layer_number]
])
layer_conv[layer_number] = DRPT(rate=drop_rate_layer)(
GroupNormalization(groups=groups)(Conv(
filters=number_of_filters_current,
kernel_size=kernelsize,
padding='same',
activation='relu',
kernel_constraint=wsconstraint)(layer_nonconv[layer_number])))
for _ in range(1, num_conv_per_pool):
layer_conv[layer_number] = DRPT(rate=drop_rate_layer)(
GroupNormalization(groups=groups)(Conv(
filters=number_of_filters_current,
kernel_size=kernelsize,
padding='same',
activation='relu',
kernel_constraint=wsconstraint)(layer_conv[layer_number])))
print('{:<30}'.format(str(layer_conv[layer_number].shape)),
'\tgroups:', groups, '\tkernel:', kernelsize, '\tdroprate:',
drop_rate_layer)
#Add CNN with output
if add_conv_layers > 0:
print()
print('Adding ' + str(add_conv_layers) + ' CNN layers to U-net')
number_of_filters_current = add_conv_filter_number
if isinstance(kernel_size, (list, )):
kernelsize = kernel_size[0]
else:
kernelsize = kernel_size
if gn_type == 'channels':
groups = int(
np.clip(number_of_filters_current / gn_param, 1,
number_of_filters_current))
else:
groups = int(np.clip(gn_param, 1, number_of_filters_current))
if add_conv_dropout_rate is None:
drop_rate_layer = dropout_rate * (np.power(
(number_of_filters_current / number_of_filters_max),
dropout_power))
else:
drop_rate_layer = add_conv_dropout_rate
for layer_CNN_number in range(add_conv_layers):
layer_conv[2 * number_of_layers_half + layer_CNN_number] = DRPT(
rate=drop_rate_layer)(GroupNormalization(groups=groups)(Conv(
number_of_filters_current,
kernel_size=kernelsize,
padding='same',
activation='relu',
kernel_constraint=wsconstraint)(
layer_conv[2 * number_of_layers_half +
layer_CNN_number - 1])))
print(
'{:<30}'.format(
str(layer_conv[2 * number_of_layers_half +
layer_CNN_number].shape)), '\tgroups:', groups,
'\tkernel:', kernelsize, '\tdroprate:', drop_rate_layer)
if isinstance(kernel_size, (list, )):
kernelsize = kernel_size[0]
else:
kernelsize = kernel_size
layer_conv[2 * number_of_layers_half + add_conv_layers] = Conv(
channels_out,
kernel_size=kernelsize,
padding='same',
activation=final_activation)(layer_conv[2 * number_of_layers_half +
add_conv_layers - 1])
print()
print('Output:',
layer_conv[2 * number_of_layers_half + add_conv_layers].shape)
print()
#build and compile U
model = Model(
inputs=[layer_nonconv[0]],
outputs=[layer_conv[2 * number_of_layers_half + add_conv_layers]],
name='unet')
print('Successfully built ' + str(len(data_shape)) + 'D U-net model')
return model
def FCN(input_shape, max_seqlen, num_classes=2, norm_max=1.0):
"""
Generate a fully convolutional neural network (FCN) model.
Parameters
----------
input_shape : tuple defining shape of the input dataset: (num_timesteps, num_channels)
num_classes : integer defining number of classes for classification task
norm_max : maximum norm for constraint
Returns
-------
model : Keras model
"""
outputdim = num_classes # number of classes
inputs = Input(shape = input_shape)
# Zero padding
pad_wd = (max_seqlen - input_shape[0])//2
x = ZeroPadding1D((pad_wd,pad_wd))(inputs)
# Stage 1
x = Conv1D(filters=32, kernel_size=7, strides=2, padding='valid', use_bias=False,
kernel_constraint=MaxNorm(norm_max, axis=[0,1,2]),
name = 'conv1', kernel_initializer=glorot_uniform(seed=0))(x)
x = LeakyReLU(alpha=0.1)(x)
x = BatchNormalization(axis=-1, momentum=0.9, name='bn_conv1',
gamma_constraint=MaxNorm(norm_max,axis=0),
beta_constraint=MaxNorm(norm_max,axis=0))(x)
# Stage 2
x = conv_block(x, ksz=3, filters=[16,16,32], stage=2, block='a', s=2, norm_max=norm_max)
x = identity_block(x, ksz=3, filters=[16,16,32], stage=2, block='b', norm_max=norm_max)
x = identity_block(x, ksz=3, filters=[16,16,32], stage=2, block='c', norm_max=norm_max)
# # Stage 3
# x = conv_block(x, ksz=3, filters=[64,64,128], stage=3, block='a', s=2)
# x = identity_block(x, ksz=3, filters=[64,64,128], stage=3, block='b')
# x = identity_block(x, ksz=3, filters=[64,64,128], stage=3, block='c')
# x = identity_block(x, ksz=3, filters=[64,64,128], stage=3, block='d')
# # Stage 4
# x = conv_block(x, ksz=3, filters=[128,128,256], stage=4, block='a', s=2)
# x = identity_block(x, ksz=3, filters=[128,128,256], stage=4, block='b')
# x = identity_block(x, ksz=3, filters=[128,128,256], stage=4, block='c')
# x = identity_block(x, ksz=3, filters=[128,128,256], stage=4, block='d')
# x = identity_block(x, ksz=3, filters=[128,128,256], stage=4, block='e')
# # Stage 5
# x = conv_block(x, ksz=3, filters=[256,256,512], stage=5, block='a', s=2)
# x = identity_block(x, ksz=3, filters=[256,256,512], stage=5, block='b')
# x = identity_block(x, ksz=3, filters=[256,256,512], stage=5, block='c')
# x = identity_block(x, ksz=3, filters=[256,256,512], stage=5, block='d')
# x = identity_block(x, ksz=3, filters=[256,256,512], stage=5, block='e')
# x = identity_block(x, ksz=3, filters=[256,256,512], stage=5, block='f')
# Output stage
x = Conv1DTranspose(x, filters=64, ksz=5, s=4, norm_max=norm_max)
x = GlobalAveragePooling1D()(x)
outputs = Dense(num_classes, activation='softmax', name='Dense',
kernel_constraint=MaxNorm(norm_max,axis=[0,1]),
bias_constraint=MaxNorm(norm_max,axis=0),
kernel_initializer=glorot_uniform(seed=0))(x)
model = Model(inputs=inputs, outputs=outputs)
return model
y_pred = rf.predict(X_test)
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
print(accuracy_score(y_test, y_pred))
##Print the feature importance by random forest
fi = pd.DataFrame({'feature': list(X_train.columns),
'importance': rf.feature_importances_}).\
sort_values('importance', ascending = False)
fi.head()
###Next we compare with a NN, as its just tabular data, so we dont need too many layers to prevent overfitting.
model = Sequential()
model.add(
Dense(64, input_dim=46, activation='relu', kernel_constraint=MaxNorm(3)))
model.add(Dropout(rate=0.2))
model.add(Dense(8, activation='relu', kernel_constraint=MaxNorm(3)))
model.add(Dropout(rate=0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss="binary_crossentropy",
optimizer='adam',
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=50,
batch_size=8)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
if USE_LSTM:
model.add(
Bidirectional(LSTM(2048,
input_shape=(54, 2048),
return_sequences=True),
merge_mode='concat')
) # 54 timesteps, 2048 feature length per timestep
model.add(
SeqSelfAttention(attention_width=15,
attention_activation=ACTIVATION_FUNC)
) # attention width 15 * 200 ms = 3 seconds
else:
model.add(Dense(2048, input_dim=2048))
model.add(Activation(ACTIVATION_FUNC))
model.add(Dropout(DROPOUT_RATE))
model.add(Dense(2048, kernel_constraint=MaxNorm(3)))
model.add(Activation(ACTIVATION_FUNC))
model.add(Dropout(DROPOUT_RATE))
model.add(Dense(2048, kernel_constraint=MaxNorm(3)))
model.add(Activation(ACTIVATION_FUNC))
model.add(Dropout(DROPOUT_RATE))
model.add(Dense(1024, kernel_constraint=MaxNorm(3)))
model.add(Activation(ACTIVATION_FUNC))
model.add(Dropout(DROPOUT_RATE))
model.add(Dense(512, kernel_constraint=MaxNorm(3)))
model.add(Activation(ACTIVATION_FUNC))
model.add(Dropout(DROPOUT_RATE))
model.add(Dense(256, kernel_constraint=MaxNorm(3)))
elif FEATURE_TYPE == 'feature-streams':
if USE_LSTM:
model.add(
def Resnet(input_shape, norm_max=1.0):
"""
Generate a fully convolutional neural network (FCN) model.
Parameters
----------
input_shape : tuple defining shape of the input dataset: (num_timesteps, num_channels)
norm_max : maximum norm for constraint
Returns
-------
model : Keras model
"""
inputs = Input(shape=input_shape)
# Stage 1
x = Conv1D(filters=32,
kernel_size=7,
strides=2,
padding='valid',
use_bias=False,
kernel_constraint=MaxNorm(norm_max, axis=[0, 1, 2]),
name='conv1',
kernel_initializer=glorot_uniform(seed=0))(inputs)
x = LeakyReLU(alpha=0.1)(x)
x = BatchNormalization(axis=-1,
momentum=0.9,
name='bn_conv1',
gamma_constraint=MaxNorm(norm_max, axis=0),
beta_constraint=MaxNorm(norm_max, axis=0))(x)
# Stage 2
x = conv_block(x,
ksz=3,
filters=[16, 16, 32],
stage=2,
block='a',
s=2,
norm_max=norm_max)
x = identity_block(x,
ksz=3,
filters=[16, 16, 32],
stage=2,
block='b',
norm_max=norm_max)
x = identity_block(x,
ksz=3,
filters=[16, 16, 32],
stage=2,
block='c',
norm_max=norm_max)
# # Stage 3
# x = conv_block(x, ksz=3, filters=[32,32,64], stage=3, block='a', s=2, norm_max=norm_max)
# x = identity_block(x, ksz=3, filters=[32,32,64], stage=3, block='b', norm_max=norm_max)
# x = identity_block(x, ksz=3, filters=[32,32,64], stage=3, block='c', norm_max=norm_max)
# x = identity_block(x, ksz=3, filters=[32,32,64], stage=3, block='d', norm_max=norm_max)
#
# # Stage 4
# x = conv_block(x, ksz=3, filters=[128,128,256], stage=4, block='a', s=2)
# x = identity_block(x, ksz=3, filters=[128,128,256], stage=4, block='b')
# x = identity_block(x, ksz=3, filters=[128,128,256], stage=4, block='c')
# x = identity_block(x, ksz=3, filters=[128,128,256], stage=4, block='d')
# x = identity_block(x, ksz=3, filters=[128,128,256], stage=4, block='e')
# # Stage 5
# x = conv_block(x, ksz=3, filters=[256,256,512], stage=5, block='a', s=2)
# x = identity_block(x, ksz=3, filters=[256,256,512], stage=5, block='b')
# x = identity_block(x, ksz=3, filters=[256,256,512], stage=5, block='c')
# x = identity_block(x, ksz=3, filters=[256,256,512], stage=5, block='d')
# x = identity_block(x, ksz=3, filters=[256,256,512], stage=5, block='e')
# x = identity_block(x, ksz=3, filters=[256,256,512], stage=5, block='f')
# Output stage
x = MaxPooling1D(pool_size=2)(x)
outputs = Flatten(name='last_layer')(x)
model = Model(inputs=inputs, outputs=outputs)
return model
def flor(input_size, output_size, learning_rate=5e-4):
"""Gated Convolucional Recurrent Neural Network by Flor."""
input_data = Input(name="input", shape=input_size)
cnn = Conv2D(filters=16,
kernel_size=(3, 3),
strides=(2, 2),
padding="same")(input_data)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=16, kernel_size=(3, 3), padding="same")(cnn)
cnn = Conv2D(filters=32,
kernel_size=(3, 3),
strides=(1, 2),
padding="same")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=32, kernel_size=(3, 3), padding="same")(cnn)
cnn = Conv2D(filters=40,
kernel_size=(2, 4),
strides=(2, 2),
padding="same")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=40,
kernel_size=(3, 3),
padding="same",
kernel_constraint=MaxNorm(4, [0, 1, 2]))(cnn)
cnn = Dropout(rate=0.2)(cnn)
cnn = Conv2D(filters=48,
kernel_size=(3, 3),
strides=(1, 2),
padding="same")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=48,
kernel_size=(3, 3),
padding="same",
kernel_constraint=MaxNorm(4, [0, 1, 2]))(cnn)
cnn = Dropout(rate=0.2)(cnn)
cnn = Conv2D(filters=56,
kernel_size=(2, 4),
strides=(2, 2),
padding="same")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=56,
kernel_size=(3, 3),
padding="same",
kernel_constraint=MaxNorm(4, [0, 1, 2]))(cnn)
cnn = Dropout(rate=0.2)(cnn)
cnn = Conv2D(filters=64,
kernel_size=(3, 3),
strides=(1, 1),
padding="same")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = MaxPooling2D(pool_size=(1, 2), strides=(1, 2), padding="valid")(cnn)
shape = cnn.get_shape()
blstm = Reshape((shape[1], shape[2] * shape[3]))(cnn)
blstm = Bidirectional(LSTM(units=128, return_sequences=True,
dropout=0.5))(blstm)
blstm = Dense(units=128)(blstm)
blstm = Bidirectional(LSTM(units=128, return_sequences=True,
dropout=0.5))(blstm)
blstm = Dense(units=output_size)(blstm)
output_data = Activation(activation="softmax")(blstm)
optimizer = RMSprop(learning_rate=learning_rate)
return (input_data, output_data, optimizer)
import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, InputLayer, Dropout, Flatten, Reshape, BatchNormalization, Conv2D, MaxPooling2D, AveragePooling2D from tensorflow.keras.optimizers import Adam from tensorflow.keras.constraints import MaxNorm # model architecture model = Sequential() model.add(InputLayer(input_shape=(X_train.shape[1], ), name='x_input')) model.add(Reshape((int(X_train.shape[1] / 13), 13, 1), input_shape=(X_train.shape[1], ))) model.add(Conv2D(10, kernel_size=5, activation='relu', padding='same', kernel_constraint=MaxNorm(3))) model.add(AveragePooling2D(pool_size=2, padding='same')) model.add(Conv2D(5, kernel_size=5, activation='relu', padding='same', kernel_constraint=MaxNorm(3))) model.add(AveragePooling2D(pool_size=2, padding='same')) model.add(Flatten()) model.add(Dense(classes, activation='softmax', name='y_pred', kernel_constraint=MaxNorm(3))) # this controls the learning rate opt = Adam(lr=0.005, beta_1=0.9, beta_2=0.999) # train the neural network model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) model.fit(X_train, Y_train, batch_size=32, epochs=9, validation_data=(X_test, Y_test), verbose=2)
def train_neural_network(self):
from tensorflow.keras.preprocessing.image import ImageDataGenerator
self.train_generator = ImageDataGenerator(
rescale=0.02,
shear_range=0.01,
zoom_range=0.02,
horizontal_flip=False).flow_from_directory(
directory=os.path.join(SCRAPER_DIR, TRAIN_FOLDER),
target_size=(img_height, img_width),
batch_size=128,
class_mode='binary',
color_mode='rgb')
self.validation_generator = ImageDataGenerator(
rescale=0.01,
shear_range=0.05,
zoom_range=0.05,
horizontal_flip=False).flow_from_directory(
directory=os.path.join(SCRAPER_DIR, VALIDATE_FOLDER),
target_size=(img_height, img_width),
batch_size=128,
class_mode='binary',
color_mode='rgb')
num_classes = 52
input_shape = (50, 15, 3)
epochs = 20
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.constraints import MaxNorm
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dropout, Flatten, Dense
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(
Conv2D(64, (3, 3),
input_shape=input_shape,
activation='relu',
padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(64, (2, 2), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(2048, activation='relu', kernel_constraint=MaxNorm(3)))
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu', kernel_constraint=MaxNorm(3)))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
from tensorflow.keras.losses import sparse_categorical_crossentropy
from tensorflow.keras import optimizers
model.compile(loss=sparse_categorical_crossentropy,
optimizer=optimizers.Adam(),
metrics=['accuracy'])
log.info(model.summary())
from tensorflow.keras.callbacks import EarlyStopping
early_stop = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=1,
verbose=1,
mode='auto')
tb = TensorBoard(log_dir='c:/tensorboard/pb',
histogram_freq=1,
write_graph=True,
write_images=True,
embeddings_freq=1,
embeddings_layer_names=False,
embeddings_metadata=False)
model.fit(self.train_generator,
epochs=epochs,
verbose=1,
validation_data=self.validation_generator,
callbacks=[early_stop])
self.model = model
score = model.evaluate(self.validation_generator, steps=52)
print('Validation loss:', score[0])
print('Validation accuracy:', score[1])
x_input = Input(batch_shape=(None, x_train.shape[1]))
e_layer = Embedding(input_dim=vocab_size, output_dim=EMBEDDING_DIM)(x_input)
e_layer = Dropout(rate=0.5)(e_layer)
for ks in [3, 4, 5]:
r_layer = Conv1D(filters=HIDDEN_DIM,
kernel_size=ks,
padding='valid',
activation='relu')(e_layer)
max_pool = GlobalMaxPooling1D()(r_layer)
flatten = Flatten()(max_pool)
conv_feature_maps.append(flatten)
r_layer = Concatenate()(conv_feature_maps)
r_layer = Dropout(rate=0.5)(r_layer)
y_output = Dense(250,
activation='relu',
kernel_constraint=MaxNorm(max_value=3.))(r_layer)
y_output = Dense(1,
activation='sigmoid',
kernel_constraint=MaxNorm(max_value=3.))(r_layer)
model = Model(x_input, y_output)
model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0001))
model.summary()
# 학습
hist = model.fit(x_train,
y_train,
validation_data=(x_test, y_test),
batch_size=256,
epochs=30)
def _build_model(self):
"""
Builds a model based on the architecture defined in the configs
The input received is already padded from the data processing module for variable sequence length.
Making is used to keep track of padded elements in the tensor. Keras layers such as Cropping1D and Concatenate
do not use masking, hence custom layer RemoveMask is used to strip masking information from the outputs for
such layers.
Architecture Logic for Multi Step Forecast -> Append the output of previous forecast step to the next one
1. Concatenate last time step aux features with outputs as outputs only contain financial fields
2. Concatenate the above output to the inputs and strip the first element in the sequence to keep the input
shape consistent
3. Repeat 1,2 for subsequent outputs
:return: compiled keras model which outputs ((output_1, mask_1), (output_2, mask_2), ...) where _1 refers to
the forecast step. For example _1 : 12 month forecast, _2 : 24 month forecast and so on
"""
outputs = []
# Masking information is only used by certain layers such as LSTM. Hence two copies of inputs are used, one for
# propagating the mask and second for storing inputs which are used in operations such as Cropping1D and
# concatenate.
inputs = x = keras.Input(shape=(self.seq_len * self.n_inputs),
name='input_financials')
prev_input = inputs
# last_time_step_aux = self.get_last_time_step_aux(x)
hidden_layer_count = 0
output_count = 0
initializer = self.initializer.get_initializer()
# TODO: make activation generic so as to use 'relu', 'leakyrelu', 'tanh' from config
for i in range(self.n_layers):
hidden_layer_count += 1
x = layers.Dense(self.n_hidden_units,
kernel_initializer=initializer,
kernel_regularizer=tf.keras.regularizers.l2(
self.config.l2_alpha),
kernel_constraint=MaxNorm(self.config.max_norm),
use_bias=False,
name='dense_%i' % hidden_layer_count)(x)
x = layers.BatchNormalization()(x)
x = layers.ReLU()(x)
x = layers.Dropout(rate=self.config.dropout)(
x, training=self.config.train)
output_count += 1
cur_output = layers.Dense(self.n_outputs,
name='OUTPUT_%i' % output_count)(x)
outputs.append(cur_output)
for fcst_step in range(1, self.forecast_steps):
print("Multi-step forecast not implemented for MLP")
raise NotImplementedError
# # output_count, lstm_count keep track of layer ids. output_count and fcst_step are not the same as one
# # fcst_step could have multiple outputs.
# output_count += 1
# cur_output = outputs[-1]
# last_time_step_fin = self.get_last_time_step(cur_output, output_count)
# # Combine latest prediction with last available aux features to make the input shape compatible
# last_time_step = layers.concatenate([last_time_step_fin, last_time_step_aux], axis=2,
# name='concat_fin_aux_%i' % fcst_step)
# # combine latest prediction with input sequence
# cur_input = layers.concatenate([prev_input, last_time_step], axis=1,
# name='combine_input_w_last_pred_%i' % fcst_step)
# cur_input = layers.Cropping1D(cropping=(1, 0), name='updated_input_w_last_pred_%i' % fcst_step)(cur_input)
# prev_input = cur_input
# # Add layer for intermediary prediction
# hidden_layer_count += 1
# dense_intm = layers.Dense(self.n_hidden_units,
# kernel_initializer=initializer,
# kernel_regularizer = tf.keras.regularizers.l2(self.config.l2_alpha),
# use_bias=False,
# name='dense_%i' % hidden_layer_count)(cur_input)
# dense_intm = layers.BatchNormalization()(dense_intm)
# dense_intm = layers.ReLU()(dense_intm)
# dense_intm = layers.Dropout(rate=self.config.dropout)(dense_intm, training=self.config.train)
# outputs.append(layers.Dense(self.n_outputs, name='OUTPUT_%i' % output_count)(dense_intm))
model = keras.Model(inputs=inputs, outputs=outputs)
return model
def _build_model(self):
"""
Builds a rnn uq range estimate model based on the architecture defined in the configs
The input received is already padded from the data processing module for variable sequence length.
Making is used to keep track of padded elements in the tensor. Keras layers such as Cropping1D and Concatenate
do not use masking, hence custom layer RemoveMask is used to strip masking information from the outputs for
such layers.
Architecture Logic for Multi Step Forecast -> Append the output of previous forecast step to the next one
1. Concatenate last time step aux features with outputs as outputs only contain financial fields
2. Concatenate the above output to the inputs and strip the first element in the sequence to keep the input
shape consistent
3. Repeat 1,2 for subsequent outputs
:return: compiled keras model which outputs (output_1, output_2, ...) where _1 refers to
the forecast step. For example _1 : 12 month forecast, _2 : 24 month forecast and so on
"""
outputs = []
# Masking information is only used by certain layers such as LSTM. Hence two copies of inputs are used, one for
# propagating the mask and second for storing inputs which are used in operations such as Cropping1D and
# concatenate.
inputs = x = keras.Input(shape=(self.seq_len, self.n_inputs),
name='input_financials')
prev_input = inputs
last_time_step_aux = self.get_last_time_step_aux(x)
lstm_count = 0
output_count = 0
initializer = self.initializer.get_initializer()
for i in range(self.n_layers):
lstm_count += 1
if self.config.rnn_cell == 'lstm':
x = layers.LSTM(
self.n_hidden_units,
kernel_initializer=initializer,
kernel_regularizer=tf.keras.regularizers.l2(
self.config.l2_alpha),
recurrent_regularizer=tf.keras.regularizers.l2(
self.config.recurrent_l2_alpha),
return_sequences=True,
kernel_constraint=MaxNorm(self.config.max_norm),
recurrent_dropout=self.config.recurrent_dropout,
name='lstm_%i' % lstm_count)(x, training=True)
x = layers.BatchNormalization()(x)
x = layers.Dropout(rate=self.config.dropout)(x, training=True)
elif self.config.rnn_cell == 'gru':
x = layers.GRU(self.n_hidden_units,
kernel_initializer=initializer,
kernel_regularizer=tf.keras.regularizers.l2(
self.config.l2_alpha),
recurrent_regularizer=tf.keras.regularizers.l2(
self.config.recurrent_l2_alpha),
return_sequences=True,
kernel_constraint=MaxNorm(self.config.max_norm),
recurrent_dropout=self.config.recurrent_dropout,
name='gru_%i' % lstm_count)(x, training=True)
x = layers.BatchNormalization()(x)
x = layers.Dropout(rate=self.config.dropout)(x, training=True)
else:
raise NotImplementedError
output_count += 1
# outputs for target values
cur_output_tar = layers.Dense(self.n_outputs,
name='OUTPUT_TARGET_%i' %
output_count)(x)
# outputs for variances of the target values
cur_output_var = layers.Dense(self.n_outputs,
name='OUTPUT_VARIANCE_%i' %
output_count)(x)
cur_output_var = SoftPlus()(cur_output_var)
outputs.append(cur_output_tar)
outputs.append(cur_output_var)
for fcst_step in range(1, self.forecast_steps):
# output_count, lstm_count keep track of layer ids. output_count and fcst_step are not the same as one
# fcst_step could have multiple outputs.
output_count += 1
cur_output = outputs[-2] # last target output
last_time_step_fin = self.get_last_time_step(
cur_output, output_count)
# Combine latest prediction with last available aux features to make the input shape compatible
last_time_step = layers.concatenate(
[last_time_step_fin, last_time_step_aux],
axis=2,
name='concat_fin_aux_%i' % fcst_step)
# combine latest prediction with input sequence
cur_input = layers.concatenate(
[prev_input, last_time_step],
axis=1,
name='combine_input_w_last_pred_%i' % fcst_step)
cur_input = layers.Cropping1D(cropping=(1, 0),
name='updated_input_w_last_pred_%i' %
fcst_step)(cur_input)
prev_input = cur_input
# Add LSTM layer for intermediary prediction
lstm_count += 1
if self.config.rnn_cell == 'lstm':
intm = layers.LSTM(
self.n_hidden_units,
return_sequences=True,
kernel_initializer=initializer,
kernel_regularizer=tf.keras.regularizers.l2(
self.config.l2_alpha),
recurrent_regularizer=tf.keras.regularizers.l2(
self.config.recurrent_l2_alpha),
kernel_constraint=MaxNorm(self.config.max_norm),
recurrent_dropout=self.config.recurrent_dropout,
name='lstm_%i' % lstm_count)(cur_input, training=True)
intm = layers.BatchNormalization()(intm)
intm = layers.Dropout(rate=self.config.dropout)(intm,
training=True)
elif self.config.rnn_cell == 'gru':
intm = layers.GRU(
self.n_hidden_units,
return_sequences=True,
kernel_initializer=initializer,
kernel_regularizer=tf.keras.regularizers.l2(
self.config.l2_alpha),
recurrent_regularizer=tf.keras.regularizers.l2(
self.config.recurrent_l2_alpha),
kernel_constraint=MaxNorm(self.config.max_norm),
recurrent_dropout=self.config.recurrent_dropout,
name='gru_%i' % lstm_count)(cur_input, training=True)
intm = layers.BatchNormalization()(intm)
intm = layers.Dropout(rate=self.config.dropout)(intm,
training=True)
else:
raise NotImplementedError
outputs.append(
layers.Dense(self.n_outputs,
name='OUTPUT_TARGET_%i' % output_count)(intm))
intm_var = layers.Dense(self.n_outputs,
name='OUTPUT_VARIANCE_%i' %
output_count)(intm)
outputs.append(SoftPlus()(intm_var))
model = keras.Model(inputs=inputs, outputs=outputs)
return model
dropout = 0.3
neurons = 500
red_rate = 0.3
constraint = 4
mom = 0.9
learning_rate = 0.01
seed = 1
set_seed(seed)
model = Sequential()
model.add(
Dense(reduce_width(neurons, red_rate, 0),
input_shape=(input_dim, ),
kernel_constraint=MaxNorm(constraint)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(
Dense(reduce_width(neurons, red_rate, 1),
kernel_constraint=MaxNorm(constraint)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(
Dense(reduce_width(neurons, red_rate, 2),
kernel_constraint=MaxNorm(constraint)))
model.add(BatchNormalization())
def flor(input_size, d_model, learning_rate):
"""
Gated Convolucional Recurrent Neural Network by Flor et al.
"""
input_data = Input(name="input", shape=input_size)
cnn = Conv2D(filters=16, kernel_size=(3,3), strides=(2,2), padding="same", kernel_initializer="he_uniform")(input_data)
cnn = PReLU(shared_axes=[1,2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=16, kernel_size=(3,3), padding="same")(cnn)
cnn = Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding="same", kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1,2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=32, kernel_size=(3,3), padding="same")(cnn)
cnn = Conv2D(filters=40, kernel_size=(2,4), strides=(2,4), padding="same", kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1,2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=40, kernel_size=(3,3), padding="same", kernel_constraint=MaxNorm(4, [0,1,2]))(cnn)
cnn = Dropout(rate=0.2)(cnn)
cnn = Conv2D(filters=48, kernel_size=(3,3), strides=(1,1), padding="same", kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1,2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=48, kernel_size=(3,3), padding="same", kernel_constraint=MaxNorm(4, [0,1,2]))(cnn)
cnn = Dropout(rate=0.2)(cnn)
cnn = Conv2D(filters=56, kernel_size=(2,4), strides=(2,4), padding="same", kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1,2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=56, kernel_size=(3,3), padding="same", kernel_constraint=MaxNorm(4, [0,1,2]))(cnn)
cnn = Dropout(rate=0.2)(cnn)
cnn = Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding="same", kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1,2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = MaxPooling2D(pool_size=(1,2), strides=(1,2), padding="valid")(cnn)
shape = cnn.get_shape()
nb_units = shape[2] * shape[3]
bgru = Reshape((shape[1], nb_units))(cnn)
bgru = Bidirectional(GRU(units=nb_units, return_sequences=True, dropout=0.5))(bgru)
bgru = Dense(units=nb_units * 2)(bgru)
bgru = Bidirectional(GRU(units=nb_units, return_sequences=True, dropout=0.5))(bgru)
output_data = Dense(units=d_model, activation="softmax")(bgru)
if learning_rate is None:
learning_rate = 5e-4
optimizer = RMSprop(learning_rate=learning_rate)
return (input_data, output_data, optimizer)
def architecture(self, input_size, d_model):
input_data = Input(name="input", shape=input_size)
cnn = Reshape((input_size[0] // 2, input_size[1] // 2,
input_size[2] * 4))(input_data)
cnn = Conv2D(filters=16,
kernel_size=(3, 3),
strides=(1, 2),
padding="same",
kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=16, kernel_size=(3, 3),
padding="same")(cnn)
cnn = Conv2D(filters=32,
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=32, kernel_size=(3, 3),
padding="same")(cnn)
cnn = Conv2D(filters=40,
kernel_size=(2, 4),
strides=(2, 4),
padding="same",
kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=40,
kernel_size=(3, 3),
padding="same",
kernel_constraint=MaxNorm(4, [0, 1, 2]))(cnn)
cnn = Dropout(rate=0.2)(cnn)
cnn = Conv2D(filters=48,
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=48,
kernel_size=(3, 3),
padding="same",
kernel_constraint=MaxNorm(4, [0, 1, 2]))(cnn)
cnn = Dropout(rate=0.2)(cnn)
cnn = Conv2D(filters=56,
kernel_size=(2, 4),
strides=(2, 4),
padding="same",
kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
cnn = FullGatedConv2D(filters=56,
kernel_size=(3, 3),
padding="same",
kernel_constraint=MaxNorm(4, [0, 1, 2]))(cnn)
cnn = Dropout(rate=0.2)(cnn)
cnn = Conv2D(filters=64,
kernel_size=(3, 3),
strides=(1, 1),
padding="same",
kernel_initializer="he_uniform")(cnn)
cnn = PReLU(shared_axes=[1, 2])(cnn)
cnn = BatchNormalization(renorm=True)(cnn)
shape = cnn.get_shape()
bgru = Reshape((shape[1], shape[2] * shape[3]))(cnn)
bgru = Bidirectional(GRU(units=128, return_sequences=True,
dropout=0.5))(bgru)
bgru = Dense(units=256)(bgru)
bgru = Bidirectional(GRU(units=128, return_sequences=True,
dropout=0.5))(bgru)
bgru = Dense(units=256)(bgru)
bgru = Bidirectional(GRU(units=128, return_sequences=True,
dropout=0.5))(bgru)
output_data = Dense(units=d_model, activation="softmax")(bgru)
return (input_data, output_data)
