def __init__(self, params):
self.params = params
cell = GRUCell(units=self.params['rnn_size'])
self.rnn = RNN(cell, return_state=True, return_sequences=True)
self.bn = BatchNormalization()
cell2 = GRUCell(units=self.params['rnn_size2'])
self.rnn2 = RNN(cell2, return_state=True, return_sequences=True)
self.dense = Dense(units=self.params['dense_size'])
def __init__(self):
super().__init__()
# TODO(lemmatizer_noattn): Define
# - source_embeddings as a masked embedding layer of source chars into args.cle_dim dimensions
self.source_embeddings = Embedding(num_source_chars, args.cle_dim, mask_zero=True)
# TODO: Define
# - source_rnn as a bidirectional GRU with args.rnn_dim units, returning _whole sequences_, summing opposite directions
self.source_rnn = Bidirectional(GRU(args.rnn_dim, return_sequences=True), merge_mode='sum')
# TODO(lemmatizer_noattn): Define
# - target_embedding as an unmasked embedding layer of target chars into args.cle_dim dimensions
# - target_rnn_cell as a GRUCell with args.rnn_dim units
# - target_output_layer as a Dense layer into `num_target_chars`
self.target_embeddings = Embedding(num_target_chars, args.cle_dim)
self.target_rnn_cell = GRUCell(args.rnn_dim)
self.target_output_layer = Dense(num_target_chars, activation=None)
# TODO: Define
# - attention_source_layer as a Dense layer with args.rnn_dim outputs
# - attention_state_layer as a Dense layer with args.rnn_dim outputs
# - attention_weight_layer as a Dense layer with 1 output
self.attention_source_layer = Dense(args.rnn_dim)
self.attention_state_layer = Dense(args.rnn_dim)
self.attention_weight_layer = Dense(1)
def make_network():
inputs = Input(shape=(image_size, image_size, 1), name='input_data')
state = Input(shape=(hidden_size, ), name='state')
x = Conv2D(32,
kernel_size=(3, 3),
strides=(2, 2),
padding='same',
activation='relu')(inputs)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Conv2D(32, kernel_size=(3, 3), strides=(2, 2), activation='relu')(x)
# x = MaxPooling2D(pool_size=(2, 2))(x)
# x = Conv2D(32, kernel_size=(3, 3), strides=(2, 2), activation='relu')(x)
# print(x.shape)
x = Flatten()(x)
x, new_state = GRUCell(hidden_size)(x, state)
action_out = Dense(7, activation='softmax', name='action')(x)
# x = Concatenate()([x, action_out])
value_out = Dense(1, activation='linear', name='reward')(x)
model = Model(inputs=[inputs, state],
outputs=[action_out, value_out, new_state])
# model.compile(optimizer='adam', loss=[loss_function, 'mse'])
# model.summary()
return model
def __init__(self, lr=1e-4, dropout_rate=0.2, units=300, beam_width=12, vocab_size=9000):
super().__init__()
self.embedding = tf.Variable(np.load('../data/embedding.npy'),
dtype=tf.float32,
name='pretrained_embedding',
trainable=False)
self.encoder = Encoder(units=units, dropout_rate=dropout_rate)
self.attention_mechanism = BahdanauAttention(units=units)
self.decoder_cell = AttentionWrapper(
GRUCell(units),
self.attention_mechanism,
attention_layer_size=units)
self.projected_layer = ProjectedLayer(self.embed.embedding)
self.sampler = tfa.seq2seq.sampler.TrainingSampler()
self.decoder = BasicDecoder(
self.decoder_cell,
self.sampler,
output_layer=self.projected_layer)
self.beam_search = BeamSearchDecoder(
self.decoder_cell,
beam_width=beam_width,
embedding_fn=lambda x: tf.nn.embedding_lookup(self.embedding, x),
output_layer=self.projected_layer)
self.vocab_size = vocab_size
self.optimizer = Adam(lr)
self.accuracy = tf.keras.metrics.Accuracy()
self.mean = tf.keras.metrics.Mean()
self.decay_lr = tf.optimizers.schedules.ExponentialDecay(lr, 1000, 0.95)
self.logger = logging.getLogger('tensorflow')
self.logger.setLevel(logging.INFO)
def __init__(self, params):
self.params = params
self.rnn_cell = GRUCell(self.params['rnn_size'])
self.dense = Dense(units=1)
self.attention = Attention(hidden_size=32,
num_heads=2,
attention_dropout=0.8)
def __init__(self, encoder=None, vocab_size=1, embedding_size=32, hidden_size=64):
"""Initialization method.
Args:
encoder (IntegerEncoder): An index to vocabulary encoder.
vocab_size (int): The size of the vocabulary.
embedding_size (int): The size of the embedding layer.
hidden_size (int): The amount of hidden neurons.
"""
logger.info('Overriding class: Generator -> GRUGenerator.')
# Overrides its parent class with any custom arguments if needed
super(GRUGenerator, self).__init__(name='G_gru')
# Creates a property for holding the used encoder
self.encoder = encoder
# Creates an embedding layer
self.embedding = Embedding(
vocab_size, embedding_size, name='embedding')
# Creates a GRU cell
self.cell = GRUCell(hidden_size, name='gru')
# Creates the RNN loop itself
self.rnn = RNN(self.cell, name='rnn_layer',
return_sequences=True,
stateful=True)
# Creates the linear (Dense) layer
self.linear = Dense(vocab_size, name='out')
logger.info('Class overrided.')
def __init__(self, params):
self.params = params
self.rnn_cell = GRUCell(self.params['rnn_size'])
self.rnn = RNN(self.rnn_cell, return_state=True, return_sequences=True)
self.dense = Dense(units=1)
self.attention = Attention(hidden_size=32,
num_heads=2,
attention_dropout=0.8)
def __init__(self, units, dropout, output_steps, output_size):
super().__init__()
self.output_steps = output_steps
self.units = units
self.lstm_cell = GRUCell(units, dropout=dropout)
self.lstm_rnn = RNN(self.lstm_cell, return_state=True)
self.dense = Dense(output_size, activation="softmax")
def __init__(self, params):
self.params = params
self.rnn_cell = GRUCell(self.params['hidden_size'])
self.rnn = RNN(self.rnn_cell, return_state=True, return_sequences=True)
self.dense = Dense(units=self.params['inputdim'])
self.attention = Attention(
hidden_size=self.params['hidden_size'],
num_heads=self.params['num_heads'],
attention_dropout=self.params['attention_dropout'])
def __init__(self, num_classes, hparams):
super().__init__()
self.state_size = hparams.hidden_dim
self.hidden_dim = hparams.hidden_dim
self.decoder_rnn = GRUCell(hparams.hidden_dim,
recurrent_initializer='glorot_uniform')
self.dense_layer = Dense(num_classes)
self.attention = LocationSensitiveAttention(hparams)
self.prev_context, self.prev_align = None, None
self.alignments = []
def __init__(self, K, conv_dim):
super(CBHG, self).__init__()
self.K = K
self.conv_bank = []
for k in range(1, self.K + 1):
x = Conv1D(128, kernel_size=k, padding='same')
self.conv_bank.append(x)
self.bn = BatchNormalization()
self.conv1 = Conv1D(conv_dim[0], kernel_size=3, padding='same')
self.bn1 = BatchNormalization()
self.conv2 = Conv1D(conv_dim[1], kernel_size=3, padding='same')
self.bn2 = BatchNormalization()
self.proj = Dense(128)
self.dense1 = Dense(128)
self.dense2 = Dense(128,
bias_initializer=tf.constant_initializer(-1.0))
self.gru_fw = GRUCell(128)
self.gru_bw = GRUCell(128)
def init_layers(self,
param_units=10,
tensor_units=5,
global_units=5,
init_lr=(1e-6, 1e-2),
timescales=1,
epsilon=1e-10,
momentum_decay_bias_init=logit(0.9),
variance_decay_bias_init=logit(0.999),
use_gradient_shortcut=True,
**kwargs):
"""Initialize layers."""
assert (init_lr[0] > 0 and init_lr[1] > 0 and epsilon > 0)
self.timescales = timescales
self.init_lr = init_lr
self.epsilon = epsilon
# Parameter, Tensor, & Global RNNs (may have different size)
self.param_rnn = GRUCell(param_units, name="param_rnn", **kwargs)
self.tensor_rnn = GRUCell(tensor_units, name="tensor_rnn", **kwargs)
self.global_rnn = GRUCell(global_units, name="global_rnn", **kwargs)
# Parameter change
self.d_theta = Dense(1,
input_shape=(param_units, ),
name="d_theta",
kernel_initializer="zeros")
# Learning rate change
self.delta_nu = Dense(1,
input_shape=(param_units, ),
name="delta_nu",
kernel_initializer="zeros")
# Momentum decay rate
self.beta_g = Dense(1,
input_shape=(param_units, ),
kernel_initializer="zeros",
bias_initializer=tf.constant_initializer(
value=momentum_decay_bias_init),
activation="sigmoid",
name="beta_g")
# Variance/scale decay rate
self.beta_lambda = Dense(1,
input_shape=(param_units, ),
kernel_initializer="zeros",
bias_initializer=tf.constant_initializer(
value=variance_decay_bias_init),
activation="sigmoid",
name="beta_lambda")
# Momentum shortcut
if use_gradient_shortcut:
self.gradient_shortcut = Dense(1,
input_shape=(timescales, ),
name="gradient_shortcut",
kernel_initializer="zeros")
else:
self.gradient_shortcut = None
# Gamma parameter
# Stored as a logit - the actual gamma used will be sigmoid(gamma)
self.gamma = tf.Variable(tf.zeros(()), trainable=True, name="gamma")
def __init__(self, rnn_utils=32, rnn_layer_num=2, dense_num=3, embedding_size=32):
super(LSPD, self).__init__()
self.input_dim = None
self.time_step = 0
self.sample_size = 0
self.rnn_utils = rnn_utils
self.gru_cell_left = []
self.gru_cell_right = []
self.batchnormals = []
self.denses = []
self.embedding = None
self.embedding_size = embedding_size
self.denses_batchnormal = []
self.rnn_layer_num = rnn_layer_num
for i in range(rnn_layer_num):
self.gru_cell_left.append(GRUCell(units=rnn_utils, activation='relu'))
self.gru_cell_right.append(GRUCell(units=rnn_utils, activation='relu'))
self.batchnormals.append(BatchNormalization())
for i in range(dense_num-1):
self.denses.append(Dense(units=32, activation='relu'))
self.denses_batchnormal.append(BatchNormalization())
self.dense = Dense(units=11, activation='softmax')
def test_gru_cell():
n_inputs = 3
n_units = 4
batch_size = 1
inputs = tx.Input(n_units=n_inputs)
gru0 = tx.GRUCell(inputs,
n_units,
activation=tf.tanh,
gate_activation=tf.sigmoid)
# applies gate after matrix multiplication and uses
# recurrent biases, this makes it compatible with cuDNN
# implementation
gru1 = GRUCell(n_units,
activation='tanh',
recurrent_activation='sigmoid',
reset_after=False,
implementation=1,
use_bias=True)
assert not hasattr(gru1, "kernel")
state0 = [s() for s in gru0.previous_state]
# get_initial_state from keras returns either a tuple or a single
# state see test_rnn_cell, but the __call__ API requires an iterable
state1 = gru1.get_initial_state(inputs, batch_size=1)
assert tx.tensor_equal(state1, state0[0])
inputs.value = tf.ones([batch_size, n_inputs])
res1 = gru1(inputs, state0)
res1_ = gru1(inputs, state0)
for r1, r2 in zip(res1, res1_):
assert tx.tensor_equal(r1, r2)
# the only difference is that keras kernels are fused together
kernel = tf.concat([w.weights.value() for w in gru0.layer_state.w],
axis=-1)
recurrent_kernel = tf.concat([u.weights for u in gru0.layer_state.u],
axis=-1)
bias = tf.concat([w.bias for w in gru0.layer_state.w], axis=-1)
assert tx.same_shape(kernel, gru1.kernel)
assert tx.same_shape(recurrent_kernel, gru1.recurrent_kernel)
assert tx.same_shape(bias, gru1.bias)
gru1.kernel = kernel
gru1.recurrent_kernel = recurrent_kernel
gru1.bias = bias
res2 = gru1(inputs, state0)
for i in range(len(res1)):
assert not tx.tensor_equal(res1[i], res2[i])
res0 = gru0()
# res0_ = gru0.state[0]()
assert tx.tensor_equal(res0, res2[0])
def __init__(self, name='gru', **kwargs):
super(GRU, self).__init__(name=name, **kwargs)
self.input_layer = InputLayer((28, 28, 1),
name='{}_input'.format(name))
self.reshape = Reshape([28, 28], name='{}_reshape'.format(
name)) # sequence of 28 elements of size 28
self.rnn = RNN(input_shape=[28, 28],
cell=GRUCell(units=128, name='{}_rnn_gru'.format(name)),
name='{}_rnn'.format(name))
self.dense = Dense(10,
activation=tf.nn.softmax,
name='{}_output'.format(name))
def __init__(self):
super().__init__()
self.source_embeddings = Embedding(num_source_chars,
args.cle_dim,
mask_zero=True)
self.source_rnn = Bidirectional(GRU(args.rnn_dim,
return_sequences=True),
merge_mode='sum')
self.target_embeddings = Embedding(num_target_chars,
args.cle_dim)
self.target_rnn_cell = GRUCell(args.rnn_dim)
self.target_output_layer = Dense(num_target_chars,
activation=None)
self.attention_source_layer = Dense(args.rnn_dim)
self.attention_state_layer = Dense(args.rnn_dim)
self.attention_weight_layer = Dense(1)
def build(self, input_shape):
assert len(input_shape) >= 2
self.kernel = self.add_weight(
name="kernel",
shape=(self.n_layers, self.channels, self.channels),
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
)
self.rnn = GRUCell(
self.channels,
kernel_initializer=self.kernel_initializer,
bias_initializer=self.bias_initializer,
kernel_regularizer=self.kernel_regularizer,
bias_regularizer=self.bias_regularizer,
activity_regularizer=self.activity_regularizer,
kernel_constraint=self.kernel_constraint,
bias_constraint=self.bias_constraint,
use_bias=self.use_bias,
)
self.built = True
def __init__(self):
super().__init__()
# TODO: Define
# - source_embeddings as a masked embedding layer of source chars into args.cle_dim dimensions
# - source_rnn as a bidirectional GRU with args.rnn_dim units, returning only the last state, summing opposite directions
self.source_embeddings = Embedding(num_source_chars,
args.cle_dim,
mask_zero=True)
self.source_rnn = Bidirectional(GRU(args.rnn_dim,
return_sequences=False),
merge_mode='sum')
# - target_embedding as an unmasked embedding layer of target chars into args.cle_dim dimensions
# - target_rnn_cell as a GRUCell with args.rnn_dim units
# - target_output_layer as a Dense layer into `num_target_chars`
self.target_embeddings = Embedding(num_target_chars,
args.cle_dim)
self.target_rnn_cell = GRUCell(args.rnn_dim)
self.target_output_layer = Dense(num_target_chars,
activation=None)
def __init__(
self,
vocab_size: Optional[int] = 1,
embedding_size: Optional[int] = 32,
hidden_size: Optional[int] = 64,
):
"""Initialization method.
Args:
vocab_size: Vocabulary size.
embedding_size: Embedding layer units.
hidden_size: Hidden layer units.
"""
logger.info("Overriding class: Base -> GRU.")
super(GRU, self).__init__(name="gru")
# Embedding layer
self.embedding = Embedding(vocab_size,
embedding_size,
name="embedding")
# GRU cell
self.cell = GRUCell(hidden_size, name="gru_cell")
# RNN layer
self.rnn = RNN(self.cell, name="rnn_layer", return_sequences=True)
# Linear (dense) layer
self.fc = Dense(vocab_size, name="out")
logger.info("Class overrided.")
logger.debug(
"Embedding: %d | Hidden: %d | Output: %d.",
embedding_size,
hidden_size,
vocab_size,
)
def build(self, input_shape):
assert len(input_shape) >= 2
F = input_shape[0][1]
if F > self.channels:
raise ValueError('channels ({}) must be greater than the number of '
'input features ({}).'.format(self.channels, F))
self.kernel = self.add_weight(name='kernel',
shape=(self.n_layers, self.channels, self.channels),
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.rnn = GRUCell(self.channels,
kernel_initializer=self.kernel_initializer,
bias_initializer=self.bias_initializer,
kernel_regularizer=self.kernel_regularizer,
bias_regularizer=self.bias_regularizer,
activity_regularizer=self.activity_regularizer,
kernel_constraint=self.kernel_constraint,
bias_constraint=self.bias_constraint,
use_bias=self.use_bias)
self.built = True
def __init__(self, params):
self.params = params
self.rnn_cell = GRUCell(units=self.params['hidden_size'])
self.rnn = RNN(self.rnn_cell, return_state=True, return_sequences=True)
self.dense = Dense(units=self.params['inputdim'])
def __init__(self, params):
self.params = params
cell = GRUCell(units=self.params['rnn_size'])
self.rnn = RNN(cell, return_state=True, return_sequences=True)
self.dense = Dense(units=1)
def gru_cell(self):
return GRUCell(num_units=self.hidden_dim)
class ScaleHierarchicalOptimizer(BaseHierarchicalPolicy):
"""Hierarchical optimizer.
Described in
"Learned Optimizers that Scale and Generalize" (Wichrowska et. al, 2017)
Keyword Args
------------
param_units : int
Number of hidden units for parameter RNN.
tensor_units : int
Number of hidden units for tensor RNN.
global_units : int
Number of hidden units for global RNN.
init_lr : float[2]
Learning rate initialization range. Actual learning rate values are
IID exp(unif(log(init_lr))).
timescales : int
Number of timescales to compute momentum for.
epsilon : float
Denominator epsilon for normalization operation in case input is 0.
momentum_decay_bias_init : float
Constant initializer for EMA momentum decay rate logit beta_g. Should
correspond to beta_1 in an Adam teacher.
variance_decay_bias_init : float
Constant initializer for EMA variance decay rate logit beta_lambda.
Should correspond to beta_2 in an Adam teacher.
use_gradient_shortcut : bool
Use shortcut connection adding linear transformation of momentum at
various timescales to direction output?
name : str
Name of optimizer network
**kwargs : dict
Passed onto tf.keras.layers.GRUCell
"""
default_name = "ScaleHierarchicalOptimizer"
def init_layers(self,
param_units=10,
tensor_units=5,
global_units=5,
init_lr=(1e-6, 1e-2),
timescales=1,
epsilon=1e-10,
momentum_decay_bias_init=logit(0.9),
variance_decay_bias_init=logit(0.999),
use_gradient_shortcut=True,
**kwargs):
"""Initialize layers."""
assert (init_lr[0] > 0 and init_lr[1] > 0 and epsilon > 0)
self.timescales = timescales
self.init_lr = init_lr
self.epsilon = epsilon
# Parameter, Tensor, & Global RNNs (may have different size)
self.param_rnn = GRUCell(param_units, name="param_rnn", **kwargs)
self.tensor_rnn = GRUCell(tensor_units, name="tensor_rnn", **kwargs)
self.global_rnn = GRUCell(global_units, name="global_rnn", **kwargs)
# Parameter change
self.d_theta = Dense(1,
input_shape=(param_units, ),
name="d_theta",
kernel_initializer="zeros")
# Learning rate change
self.delta_nu = Dense(1,
input_shape=(param_units, ),
name="delta_nu",
kernel_initializer="zeros")
# Momentum decay rate
self.beta_g = Dense(1,
input_shape=(param_units, ),
kernel_initializer="zeros",
bias_initializer=tf.constant_initializer(
value=momentum_decay_bias_init),
activation="sigmoid",
name="beta_g")
# Variance/scale decay rate
self.beta_lambda = Dense(1,
input_shape=(param_units, ),
kernel_initializer="zeros",
bias_initializer=tf.constant_initializer(
value=variance_decay_bias_init),
activation="sigmoid",
name="beta_lambda")
# Momentum shortcut
if use_gradient_shortcut:
self.gradient_shortcut = Dense(1,
input_shape=(timescales, ),
name="gradient_shortcut",
kernel_initializer="zeros")
else:
self.gradient_shortcut = None
# Gamma parameter
# Stored as a logit - the actual gamma used will be sigmoid(gamma)
self.gamma = tf.Variable(tf.zeros(()), trainable=True, name="gamma")
def call_global(self, states, global_state, training=False):
"""Equation 12.
Global RNN. Inputs are prepared (except for final mean) in ``call``.
"""
# [1, units] -> [num tensors, 1, units] -> [1, units]
inputs = tf.reduce_mean(
tf.stack([state["tensor"] for state in states]), 0)
global_state_new, _ = self.global_rnn(inputs, global_state)
return global_state_new
def _new_momentum_variance(self, grads, states, states_new):
"""Equation 1, 2, 3, 13.
Helper function for scaled momentum update
"""
# Base decay
# Eq 13
# [var size, 1] -> [*var shape]
shape = tf.shape(grads)
beta_g = tf.reshape(self.beta_g(states["param"]), shape)
beta_lambda = tf.reshape(self.beta_lambda(states["param"]), shape)
# New momentum, variance
# Eq 1, 2
states_new["scaling"] = [
rms_momentum(grads,
g_bar,
lambda_,
beta_1=beta_g**(0.5**s),
beta_2=beta_lambda**(0.5**s))
for s, (g_bar, lambda_) in enumerate(states["scaling"])
]
# Scaled momentum
_m = [
g_bar / tf.sqrt(lambda_ + self.epsilon)
for g_bar, lambda_ in states_new["scaling"]
]
# m_t: [timescales, *var shape] -> [var size, timescales]
return tf.transpose(tf.reshape(tf.stack(_m), [self.timescales, -1]))
def _relative_log_gradient_magnitude(self, states, states_new):
"""Equation 4.
Helper function for relative log gradient magnitudes
"""
log_lambdas = tf.math.log(
tf.stack([lambda_ for g_bar, lambda_ in states_new["scaling"]]) +
self.epsilon)
_gamma = log_lambdas - tf.reduce_mean(log_lambdas, axis=0)
# gamma_t: [timescales, *var shape] -> [var size, timescales]
return tf.transpose(tf.reshape(_gamma, [self.timescales, -1]))
def _parameterized_change(self, param, states, states_new, m):
"""Equation 5, 7, 8.
Helper function for parameter change explicitly parameterized into
direction and learning rate
Notes
-----
(1) Direction is no longer explicitly parameterized, as specified by
appendix D.3 in Wichrowska et al.
(2) A shortcut connection is include as per appendix B.1.
"""
# New learning rate
# Eq 7, 8
d_eta = tf.reshape(self.delta_nu(states_new["param"]), tf.shape(param))
eta = d_eta + states["eta_bar"]
sg = tf.nn.sigmoid(self.gamma)
states_new["eta_bar"] = (sg * states["eta_bar"] + (1 - sg) * eta)
# Relative log learning rate
# Eq Unnamed, end of sec 3.2.4
states_new["eta_rel"] = tf.reshape(eta - tf.math.reduce_mean(eta),
[-1, 1])
# Direction
# Eq 5, using the update given in Appendix D.3
d_theta = self.d_theta(states_new["param"])
if self.gradient_shortcut:
d_theta += self.gradient_shortcut(m)
return tf.exp(eta) * tf.reshape(d_theta, tf.shape(param))
def call(self, param, grads, states, global_state, training=False):
"""Optimizer Update.
Notes
-----
The state indices in Wichrowska et al. are incorrect, and should be:
(1) g_bar^n, lambda^n = EMA(g_bar^n-1, g^n), EMA(lambda^n-1, g^n)
instead of EMA(..., g^n-1), etc
(2) h^n = RNN(x^n, h^n-1) instead of h^n+1 = RNN(x^n, h^n)
Then, the g^n -> g_bar^n, lambda^n -> m^n -> h^n -> d^n data flow
occurs within the same step instead of across 2 steps. This fix is
reflected in the original Scale code.
In order to reduce state size, the state update computation is split:
(1) Compute beta_g, beta_lambda, m.
(2) Update Parameter & Tensor RNN.
(3) Compute eta, d. This step only depends on the parameter RNN,
so the Global RNN being updated after this does not matter.
(4) Update Global RNN.
eta_rel is the only "transient" (i.e. not RNN hidden states, momentum,
variance, learning rate) product stored in the optimizer state.
"""
states_new = {}
# Prerequisites ("Momentum and variance at various timescales")
# Eq 1, 2, 3, 13
m = self._new_momentum_variance(grads, states, states_new)
# Eq 4
gamma = self._relative_log_gradient_magnitude(states, states_new)
# Param RNN
# inputs = [var size, features]
param_in = tf.concat(
[
# x^n:
m,
gamma,
states["eta_rel"],
# h_tensor: [1, hidden size] -> [var size, hidden size]
tf.tile(states["tensor"], [tf.size(param), 1]),
# h_global: [1, hidden size] -> [var size, hidden size]
tf.tile(global_state, [tf.size(param), 1]),
],
1)
# RNN Update
# Eq 10
states_new["param"], _ = self.param_rnn(param_in, states["param"])
# Eq 11
tensor_in = tf.concat([
tf.math.reduce_mean(states_new["param"], 0, keepdims=True),
global_state
], 1)
states_new["tensor"], _ = self.tensor_rnn(tensor_in, states["tensor"])
# Eq 5, 7, 8
delta_theta = self._parameterized_change(param, states, states_new, m)
return delta_theta, states_new
def get_initial_state(self, var):
"""Get initial model state as a dictionary."""
batch_size = tf.size(var)
return {
"scaling": [(tf.zeros(tf.shape(var)), tf.zeros(tf.shape(var)))
for s in range(self.timescales)],
"param":
self.param_rnn.get_initial_state(batch_size=batch_size,
dtype=tf.float32),
"tensor":
self.tensor_rnn.get_initial_state(batch_size=1, dtype=tf.float32),
"eta_bar":
tf.random.uniform(shape=tf.shape(var),
minval=tf.math.log(self.init_lr[0]),
maxval=tf.math.log(self.init_lr[1])),
"eta_rel":
tf.zeros([batch_size, 1]),
}
def get_initial_state_global(self):
"""Initialize global hidden state."""
return self.global_rnn.get_initial_state(batch_size=1,
dtype=tf.float32)
def __init__(self, params):
self.params = params
self.predict_window_sizes = params['predict_sequence_length']
self.rnn_cell = GRUCell(self.params['rnn_size'])
self.dense = Dense(units=1)
self.attention = Attention(hidden_size=32, num_heads=2, attention_dropout=0.8)
def __init__(self, params):
cell = GRUCell(units=params['rnn_units'])
self.rnn = RNN(cell, return_state=True, return_sequences=True)
self.dense = Dense(units=2)
self.activate = Activate()
