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ML_ECMO/code/synthetic_data_processing/train_model.py
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train_model.py
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import tensorflow as tf
from tensorflow import keras
from tensorflow.compat.v1 import train
from tensorflow.keras.layers import Dense, Flatten, Conv1D, MaxPooling1D, GlobalAveragePooling1D, Dropout, TimeDistributed, LSTM
from tensorflow.keras.activations import relu, sigmoid, elu, tanh
tf.compat.v1.enable_eager_execution()
def train_model(model, train_input, train_target, dim_recurrent, learning_rate, num_epochs=25, batch_size=10, loss_tab=[]):
"""
Train a classic recurrent neural network model using recurrent state.
:param model: Model to train
:param train_input: Train input tensor containing input data
:param train_target: Train target tensor containing labels
:param dim_recurrent: Dimension of the recurrent space
:param learning_rate: Learning rate used for the iterative optimization method
:param num_epochs: Number of epochs used to train the network
:param batch_size: Number of samples per batch for the training of the network
:param loss_tab: List that will contains the values of the loss for each batch iteration and time step
"""
# Define the optimization method
optimizer = train.AdamOptimizer(learning_rate=learning_rate)
# Define the loss
mse = tf.keras.losses.MeanSquaredError()
global_step = tf.Variable(0)
nb_samples, time_steps = train_input.shape[0].value, train_input.shape[1].value
# Iterate over the epochs
for epoch in range(num_epochs):
# Shuffle the samples
indices = tf.range(start=0, limit=tf.shape(train_input)[0], dtype=tf.int32)
shuffled_indices = tf.random.shuffle(indices)
train_input = tf.gather(train_input, shuffled_indices)
train_target = tf.gather(train_target, shuffled_indices)
# Initialize each state at zero
states = list(tf.zeros(shape=[dim_recurrent]) for i in range(train_input.shape[0]))
# Loop over the samples, take batches of size 'mini_batch_size'
for b in range(0, nb_samples, batch_size):
# For each sample we initialize the loss_value to zero
loss_value = 0
for t in range(train_input.shape[1]):
with tf.GradientTape() as tape:
# Make a prediction at current time
output, states[b:b + batch_size] = model(train_input[b:b + batch_size, t], states[b:b + batch_size])
# Accumulate the loss
loss_value += mse(output, train_target[b:b + batch_size, t])
# Update the gradients
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)
loss_tab.append(loss_value/time_steps)
print("Epoch : ", epoch)
def train_model_LSTM(model, train_input, train_target, dim_recurrent, learning_rate, num_epochs=25, batch_size=10, loss_tab=[]):
"""
Train a classic long short term memory network model using recurrent state and candidate state.
:param model: Model to train
:param train_input: Train input tensor containing input data
:param train_target: Train target tensor containing labels
:param dim_recurrent: Dimension of the recurrent space
:param learning_rate: Learning rate used for the iterative optimization method
:param num_epochs: Number of epochs used to train the network
:param batch_size: Number of samples per batch for the training of the network
:param loss_tab: List that will contains the values of the loss for each batch iteration and time step
"""
# Define the optimization method
optimizer = train.AdamOptimizer(learning_rate=learning_rate)
# Define the loss
mse = tf.keras.losses.MeanSquaredError()
global_step = tf.Variable(0)
nb_samples, time_steps = train_input.shape[0].value, train_input.shape[1].value
# Iterate over the epochs
for epoch in range(num_epochs):
# Shuffle the samples
indices = tf.range(start=0, limit=tf.shape(train_input)[0], dtype=tf.int32)
shuffled_indices = tf.random.shuffle(indices)
train_input = tf.gather(train_input, shuffled_indices)
train_target = tf.gather(train_target, shuffled_indices)
# Initialize each state at zero
states = list(tf.zeros(shape=[dim_recurrent]) for i in range(train_input.shape[0]))
candidates = list(tf.zeros(shape=[dim_recurrent]) for i in range(train_input.shape[0]))
# Loop over the samples, take batches of size 'mini_batch_size'
for b in range(0, nb_samples, batch_size):
# For each sample we initialize the loss_value to zero
loss_value = 0
for t in range(train_input.shape[1]):
with tf.GradientTape() as tape:
# Make a prediction at current time
output, states[b:b + batch_size], candidates[b:b + batch_size] \
= model(train_input[b:b + batch_size, t], states[b:b + batch_size], candidates[b:b + batch_size])
# Accumulate the loss
loss_value += mse(output, train_target[b:b + batch_size, t])
# Update the gradients
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)
loss_tab.append(loss_value/time_steps)
print("Epoch : ", epoch)
def train_model_mv(model, train_input, train_target, NAN_targets, states, learning_rate, num_epochs=25,
mini_batch_size=10, loss_tab=[]):
"""
Train model with missing values data. In construction.
"""
optimizer = train.AdamOptimizer(learning_rate=learning_rate)
mse = tf.keras.losses.MeanSquaredError()
global_step = tf.Variable(0)
for epoch in range(num_epochs):
#perm = np.random.permutation(500)
#train_input = train_input[perm, :, :]
#train_target = train_target[perm, :, :]
#states = list(train_target[i, 0, :] for i in range(500))
states = list(tf.zeros(shape=[states[0].shape[0]]) for i in range(train_input.shape[0]))
for b in range(0, train_input.shape[0], mini_batch_size):
loss_value = 0
for t in range(train_input.shape[1]):
with tf.GradientTape() as tape:
output, states[b:b + mini_batch_size] = model(train_input[b:b + mini_batch_size, t], states[b:b + mini_batch_size])
#loss_value += mse(output, train_target[b:b + mini_batch_size, t])
loss_value += mse(tf.boolean_mask(output, tf.logical_not(NAN_targets[b:b + mini_batch_size, t])),
tf.boolean_mask(train_target[b:b + mini_batch_size, t],
tf.logical_not(NAN_targets[b:b + mini_batch_size, t])))
# Update the gradients
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)
#print(loss_value)
loss_tab.append(loss_value/61)
print("Epoch : ", epoch)
print("Loss : ", loss_value)
def train_model_2cells(model, train_input, train_target, states, learning_rate, num_epochs=25, batch_size=10, loss_tab=[]):
"""
Train recurrent neural network with two cells.
:param model: Model to train
:param train_input: Train input tensor containing input data
:param train_target: Train target tensor containing labels
:param learning_rate: Learning rate used for the iterative optimization method
:param num_epochs: Number of epochs used to train the network
:param batch_size: Number of samples per batch for the training of the network
:param loss_tab: List that will contains the values of the loss for each batch iteration and time step
"""
# Define the optimization method
optimizer = train.AdamOptimizer(learning_rate=learning_rate)
# Define the loss
mse = tf.keras.losses.MeanSquaredError()
global_step = tf.Variable(0)
nb_samples, time_steps = train_input.shape[0].value, train_input.shape[1].value
for epoch in range(num_epochs):
states = list(tf.zeros(shape=[states[0].shape[0]]) for i in range(nb_samples))
for b in range(0, train_input.shape[0], batch_size):
loss_value = 0
for t in range(1, train_input.shape[1]):
with tf.GradientTape() as tape:
input_data = list(train_input[b:b + batch_size, i] for i in range (t-1,t+1))
output, states[b:b + batch_size] = model(input_data, states[b:b + batch_size])
loss_value += mse(output, train_target[b:b + batch_size, t])
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)
loss_tab.append(loss_value/time_steps)
print(epoch)
def train_model_multistep(model, train_input, train_target, learning_rate, num_epochs=25, batch_size=10, loss_tab=[]):
"""
Train multi-step recurrent neural network with one step backward in time.
:param model: Model to train
:param train_input: Train input tensor containing input data
:param train_target: Train target tensor containing labels
:param learning_rate: Learning rate used for the iterative optimization method
:param num_epochs: Number of epochs used to train the network
:param batch_size: Number of samples per batch for the training of the network
:param loss_tab: List that will contains the values of the loss for each batch iteration and time step
"""
# Define the optimization method
optimizer = train.AdamOptimizer(learning_rate=learning_rate)
# Define the loss
mse = tf.keras.losses.MeanSquaredError()
global_step = tf.Variable(0)
nb_samples, time_steps = train_input.shape[0].value, train_input.shape[1].value
# Iterate over the epochs
for epoch in range(num_epochs):
# Shuffle the samples
indices = tf.range(start=0, limit=tf.shape(train_input)[0], dtype=tf.int32)
shuffled_indices = tf.random.shuffle(indices)
train_input = tf.gather(train_input, shuffled_indices)
train_target = tf.gather(train_target, shuffled_indices)
# Loop over the samples, take batches of size 'mini_batch_size'
for b in range(0, nb_samples, batch_size):
# For each sample we initialize the loss_value to zero
loss_value = 0
for t in range(1, train_input.shape[1]):
with tf.GradientTape() as tape:
# Make a prediction at current time
output = model(train_input[b:b + batch_size, t], train_input[b:b + batch_size, t-1],
train_target[b:b + batch_size, t-1])
# Accumulate the loss
loss_value += mse(output, train_target[b:b + batch_size, t])
# Update the gradients
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)
loss_tab.append(loss_value/time_steps)
print("Epoch : ", epoch)
def train_model_mlp(model, train_input, train_target, learning_rate, num_epochs=25, batch_size=10, loss_tab=[]):
"""
Train multi-step recurrent neural network with one step backward in time.
:param model: Model to train
:param train_input: Train input tensor containing input data
:param train_target: Train target tensor containing labels
:param learning_rate: Learning rate used for the iterative optimization method
:param num_epochs: Number of epochs used to train the network
:param batch_size: Number of samples per batch for the training of the network
:param loss_tab: List that will contains the values of the loss for each batch iteration and time step
"""
# Define the optimization method
optimizer = train.AdamOptimizer(learning_rate=learning_rate)
# Define the loss
mse = tf.keras.losses.MeanSquaredError()
global_step = tf.Variable(0)
nb_samples, time_steps = train_input.shape[0].value, train_input.shape[1].value
# Iterate over the epochs
for epoch in range(num_epochs):
# Shuffle the samples
indices = tf.range(start=0, limit=tf.shape(train_input)[0], dtype=tf.int32)
shuffled_indices = tf.random.shuffle(indices)
train_input = tf.gather(train_input, shuffled_indices)
train_target = tf.gather(train_target, shuffled_indices)
# Loop over the samples, take batches of size 'mini_batch_size'
for b in range(0, nb_samples, batch_size):
# For each sample we initialize the loss_value to zero
loss_value = 0
for t in range(1, train_input.shape[1]):
with tf.GradientTape() as tape:
# Make a prediction at current time
output = model(train_input[b:b + batch_size, t], train_input[b:b + batch_size, t-1],
train_target[b:b + batch_size, t-1], t)
# Accumulate the loss
loss_value += mse(output, train_target[b:b + batch_size, t])
# Update the gradients
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)
loss_tab.append(loss_value/time_steps)
print("Epoch : ", epoch)
def train_model_mlp_2(model, train_input, train_target, learning_rate, num_epochs=25, batch_size=10, loss_tab=[]):
"""
Train multi-step recurrent neural network with one step backward in time.
:param model: Model to train
:param train_input: Train input tensor containing input data
:param train_target: Train target tensor containing labels
:param learning_rate: Learning rate used for the iterative optimization method
:param num_epochs: Number of epochs used to train the network
:param batch_size: Number of samples per batch for the training of the network
:param loss_tab: List that will contains the values of the loss for each batch iteration and time step
"""
# Define the optimization method
optimizer = train.AdamOptimizer(learning_rate=learning_rate)
# Define the loss
mse = tf.keras.losses.MeanSquaredError()
global_step = tf.Variable(0)
nb_samples, time_steps = train_input.shape[0].value, train_input.shape[1].value
# Iterate over the epochs
for epoch in range(num_epochs):
# Shuffle the samples
indices = tf.range(start=0, limit=tf.shape(train_input)[0], dtype=tf.int32)
shuffled_indices = tf.random.shuffle(indices)
train_input = tf.gather(train_input, shuffled_indices)
train_target = tf.gather(train_target, shuffled_indices)
# Loop over the samples, take batches of size 'mini_batch_size'
for b in range(0, nb_samples, batch_size):
# For each sample we initialize the loss_value to zero
loss_value = 0
for t in range(1, train_input.shape[1]):
with tf.GradientTape() as tape:
# Make a prediction at current time
if t == 1:
output = model(train_input[b:b + batch_size, t], train_input[b:b + batch_size, t-1],
train_target[b:b + batch_size, t-1], t)
else:
output = model(train_input[b:b + batch_size, t], train_input[b:b + batch_size, t - 1],
output, t)
# Accumulate the loss
loss_value += mse(output, train_target[b:b + batch_size, t])
# Update the gradients
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)
loss_tab.append(loss_value/time_steps)
print("Epoch : ", epoch)
def train_model_mlp_3(model, train_input, train_target, learning_rate, num_epochs=25, batch_size=10, loss_tab=[]):
"""
Train multi-step recurrent neural network with one step backward in time.
:param model: Model to train
:param train_input: Train input tensor containing input data
:param train_target: Train target tensor containing labels
:param learning_rate: Learning rate used for the iterative optimization method
:param num_epochs: Number of epochs used to train the network
:param batch_size: Number of samples per batch for the training of the network
:param loss_tab: List that will contains the values of the loss for each batch iteration and time step
"""
# Define the optimization method
optimizer = train.AdamOptimizer(learning_rate=learning_rate)
# Define the loss
mse = tf.keras.losses.MeanSquaredError()
global_step = tf.Variable(0)
nb_samples, time_steps = train_input.shape[0].value, train_input.shape[1].value
# Iterate over the epochs
for epoch in range(num_epochs):
# Shuffle the samples
indices = tf.range(start=0, limit=tf.shape(train_input)[0], dtype=tf.int32)
shuffled_indices = tf.random.shuffle(indices)
train_input = tf.gather(train_input, shuffled_indices)
train_target = tf.gather(train_target, shuffled_indices)
# Loop over the samples, take batches of size 'mini_batch_size'
for b in range(0, nb_samples, batch_size):
# For each sample we initialize the loss_value to zero
loss_value = 0
outputs = []
for t in range(3, train_input.shape[1]):
with tf.GradientTape() as tape:
# Make a prediction at current time
if t >= 3:
output = model(train_input[b:b + batch_size, t], train_target[b:b + batch_size, t-1],
train_target[b:b + batch_size, t-2],
train_target[b:b + batch_size, t-3], t)
outputs.append(output)
#elif t == 4:
# output = model(train_input[b:b + batch_size, t], outputs[0],
# train_target[b:b + batch_size, t - 2],
# train_target[b:b + batch_size, t - 3], t)
# outputs.append(output)
# elif t == 5:
# output = model(train_input[b:b + batch_size, t], outputs[1],
# outputs[0], train_target[b:b + batch_size, t - 3], t)
# outputs.append(output)
# else:
# output = model(train_input[b:b + batch_size, t], outputs[2],
# outputs[1], outputs[0], t)
# outputs.pop(0)
# outputs.append(output)
# Accumulate the loss
loss_value += mse(output, train_target[b:b + batch_size, t])
# Update the gradients
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)
loss_tab.append(loss_value/time_steps)
print("Epoch : ", epoch)
def train_model_mlp_4(model, train_input, train_target, learning_rate, num_epochs=25, batch_size=10, loss_tab=[]):
"""
Train multi-step recurrent neural network with one step backward in time.
:param model: Model to train
:param train_input: Train input tensor containing input data
:param train_target: Train target tensor containing labels
:param learning_rate: Learning rate used for the iterative optimization method
:param num_epochs: Number of epochs used to train the network
:param batch_size: Number of samples per batch for the training of the network
:param loss_tab: List that will contains the values of the loss for each batch iteration and time step
"""
# Define the optimization method
optimizer = train.AdamOptimizer(learning_rate=learning_rate)
# Define the loss
mse = tf.keras.losses.MeanSquaredError()
global_step = tf.Variable(0)
nb_samples, time_steps = train_input.shape[0].value, train_input.shape[1].value
# Iterate over the epochs
for epoch in range(num_epochs):
# Shuffle the samples
indices = tf.range(start=0, limit=tf.shape(train_input)[0], dtype=tf.int32)
shuffled_indices = tf.random.shuffle(indices)
train_input = tf.gather(train_input, shuffled_indices)
train_target = tf.gather(train_target, shuffled_indices)
# Loop over the samples, take batches of size 'mini_batch_size'
for b in range(0, nb_samples, batch_size):
# For each sample we initialize the loss_value to zero
loss_value = 0
outputs = []
for t in range(3, train_input.shape[1]):
with tf.GradientTape() as tape:
# Make a prediction at current time
if t >= 3:
output = model(train_input[b:b + batch_size, t], train_input[b:b + batch_size, t-1],
train_target[b:b + batch_size, t-1],
train_target[b:b + batch_size, t-2], t)
outputs.append(output)
#elif t == 4:
# output = model(train_input[b:b + batch_size, t], outputs[0],
# train_target[b:b + batch_size, t - 2],
# train_target[b:b + batch_size, t - 3], t)
# outputs.append(output)
# elif t == 5:
# output = model(train_input[b:b + batch_size, t], outputs[1],
# outputs[0], train_target[b:b + batch_size, t - 3], t)
# outputs.append(output)
# else:
# output = model(train_input[b:b + batch_size, t], outputs[2],
# outputs[1], outputs[0], t)
# outputs.pop(0)
# outputs.append(output)
# Accumulate the loss
loss_value += mse(output, train_target[b:b + batch_size, t])
# Update the gradients
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)
loss_tab.append(loss_value/time_steps)
print("Epoch : ", epoch)
def train_model_multistep_2(model, train_input, train_target, learning_rate, num_epochs=25, batch_size=10, loss_tab=[]):
"""
Train multi-step recurrent neural network with one step backward in time.
:param model: Model to train
:param train_input: Train input tensor containing input data
:param train_target: Train target tensor containing labels
:param learning_rate: Learning rate used for the iterative optimization method
:param num_epochs: Number of epochs used to train the network
:param batch_size: Number of samples per batch for the training of the network
:param loss_tab: List that will contains the values of the loss for each batch iteration and time step
"""
# Define the optimization method
optimizer = train.AdamOptimizer(learning_rate=learning_rate)
# Define the loss
mse = tf.keras.losses.MeanSquaredError()
global_step = tf.Variable(0)
nb_samples, time_steps = train_input.shape[0].value, train_input.shape[1].value
# Iterate over the epochs
for epoch in range(num_epochs):
print("Epoch : ", epoch)
# Shuffle the samples
indices = tf.range(start=0, limit=tf.shape(train_input)[0], dtype=tf.int32)
shuffled_indices = tf.random.shuffle(indices)
train_input = tf.gather(train_input, shuffled_indices)
train_target = tf.gather(train_target, shuffled_indices)
# Loop over the samples, take batches of size 'mini_batch_size'
for b in range(0, nb_samples, batch_size):
# For each sample we initialize the loss_value to zero
loss_value = 0
for t in range(1, train_input.shape[1]):
with tf.GradientTape() as tape:
if t == 1:
# Make a prediction at current time
output = model(train_input[b:b + batch_size, t], train_input[b:b + batch_size, t-1],
train_target[b:b + batch_size, t-1])
else:
output = model(train_input[b:b + batch_size, t], train_input[b:b + batch_size, t - 1],
output)
# Accumulate the loss
loss_value += mse(output, train_target[b:b + batch_size, t])
# Update the gradients
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)
loss_tab.append(loss_value/time_steps)
def train_model_multisteps(model, train_input, train_target, learning_rate, num_epochs=25, batch_size=10, loss_tab=[]):
"""
Train multi-step recurrent neural network with two step backward in time.
:param model: Model to train
:param train_input: Train input tensor containing input data
:param train_target: Train target tensor containing labels
:param learning_rate: Learning rate used for the iterative optimization method
:param num_epochs: Number of epochs used to train the network
:param batch_size: Number of samples per batch for the training of the network
:param loss_tab: List that will contains the values of the loss for each batch iteration and time step
"""
# Define the optimization method
optimizer = train.AdamOptimizer(learning_rate=learning_rate)
# Define the loss
mse = tf.keras.losses.MeanSquaredError()
global_step = tf.Variable(0)
nb_samples, time_steps = train_input.shape[0].value, train_input.shape[1].value
# Iterate over the epochs
for epoch in range(num_epochs):
# Shuffle the samples
indices = tf.range(start=0, limit=tf.shape(train_input)[0], dtype=tf.int32)
shuffled_indices = tf.random.shuffle(indices)
train_input = tf.gather(train_input, shuffled_indices)
train_target = tf.gather(train_target, shuffled_indices)
# Loop over the samples, take batches of size 'mini_batch_size'
for b in range(0, nb_samples, batch_size):
# For each sample we initialize the loss_value to zero
loss_value = 0
for t in range(2, train_input.shape[1]):
with tf.GradientTape() as tape:
# Make a prediction at current time
output = model(train_input[b:b + batch_size, t], train_input[b:b + batch_size, t-1],
train_input[b:b + batch_size, t-2], train_target[b:b + batch_size, t-1],
train_target[b:b + batch_size, t-2])
# Accumulate the loss
loss_value += mse(output, train_target[b:b + batch_size, t])
# Update the gradients
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step)
loss_tab.append(loss_value/time_steps)
print("Epoch : ", epoch)
def tempconvnet(offset, dim_input, dim_output, loss, optimizer):
"""
Build a classic convolutional neural network (CNN)
:param offset: Size of the window used to apply the convolution
:param dim_input: Number of input signals per sample
:param dim_output: Number of output parameters per sample
:param loss: Loss used to train the network
:param optimizer: Optimization method used to train the network
:return: Convolutional neural network (CNN)
"""
convmodel = keras.Sequential([
Conv1D(64, 2, activation='relu', input_shape=(offset, dim_input)),
Conv1D(32, 2, activation='relu'),
GlobalAveragePooling1D(),
Dropout(0.5),
Dense(dim_output, activation='relu')])
convmodel.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])
return convmodel
def tempconvnetdilated(offset, dim_input, dim_output, loss, optimizer):
"""
Build a dilated causal convolutional neural network (DCCNN)
:param offset: Size of the window used to apply the convolution
:param dim_input: Number of input signals per sample
:param dim_output: Number of output parameters per sample
:param loss: Loss used to train the network
:param optimizer: Optimization method used to train the network
:return: Dilated causal convolutional neural network (DCCNN)
"""
convmodel = keras.Sequential([
Conv1D(32, 2, activation='relu', input_shape=(offset, dim_input), padding='causal', dilation_rate=2),
#MaxPooling1D(2),
Conv1D(32, 2, activation='relu', padding='causal', dilation_rate=4),
Conv1D(32, 2, activation='relu', padding='causal', dilation_rate=8),
Conv1D(32, 2, activation='relu', padding='causal', dilation_rate=16),
Conv1D(32, 2, activation='relu', padding='causal', dilation_rate=32),
Conv1D(32, 2, activation='relu', padding='causal', dilation_rate=64),
GlobalAveragePooling1D(),
Dense(128, activation='relu'),
Dropout(0.5),
Dense(dim_output, activation='relu')])
convmodel.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])
return convmodel
def train_tempconvnet(convnet, train_input, train_target, time_steps, offset, batch_size, nb_epochs):
"""
Train a 1D convolutional neural network
:param convnet: Neural network
:param train_input: Input tensor
:param train_target: Labels, target tensor
:param time_steps: Number of time steps
:param offset: Size of the window used to apply the convolution
:param batch_size: Size of the batches used to train the network
:param nb_epochs: Number of epochs used to train the network
"""
for t in range(time_steps-offset):
convnet.fit(train_input[:, t:offset+t], train_target[:, offset+t], batch_size=batch_size, epochs=nb_epochs)
def lstm_net(dim_input, dim_output, window):
model = keras.Sequential([
keras.layers.LSTM(100, input_shape=(window, dim_input)),
keras.layers.LSTM(200),
keras.layers.LSTM(100),
Dense(128, activation='sigmoid'),
Dropout(0.3),
Dense(dim_output, activation='sigmoid')])
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
model.summary()
return model
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