def _build_graph(self, layer, previous_state):
with layer_scope(self):
if previous_state is None:
input_batch = tf.shape(layer.tensor)[0]
zero_state = tf.zeros([input_batch, self.n_units])
self.previous_state = tx.TensorLayer(zero_state, self.n_units)
if self.share_state_with is None:
# determines the weight of the previous state
# we could add the bias at the end but this way we just define a single bias for the r unit
self.r_current_w = tx.Linear(layer,
self.n_units,
bias=True,
weight_init=self.init,
name="r_current_w")
self.r_recurrent_w = tx.Linear(self.previous_state,
self.n_units,
bias=False,
weight_init=self.recurrent_init,
name="r_current_w")
self.u_current_w = tx.Linear(layer,
self.n_units,
bias=True,
weight_init=self.init,
name="u_current_w")
self.u_recurrent_w = tx.Linear(self.previous_state,
self.n_units,
bias=False,
weight_init=self.recurrent_init,
name="u_current_w")
self.current_w = tx.Linear(layer,
self.n_units,
bias=True,
weight_init=self.init,
name="current_w")
self.recurrent_w = tx.Linear(self.previous_state,
self.n_units,
bias=False,
weight_init=self.recurrent_init,
name="recurrent_w")
# kernel_gate = tx.Activation()
kernel_act = tx.Activation(kernel_linear, self.activation)
self.kernel = tx.Compose(kernel_linear, kernel_act)
else:
self.kernel = self.share_state_with.kernel.reuse_with(layer)
self.recurrent_kernel = self.share_state_with.recurrent_kernel.reuse_with(
self.previous_state)
r_state = tx.Add(r_current_w, r_recurrent_w)
r_state = tx.Bias(r_state)
r_gate = tx.Activation(r_state, fn=tx.sigmoid, name="r_gate")
# """Gated recurrent unit (GRU) with nunits cells."""
return self.kernel.tensor + self.recurrent_kernel.tensor
def _build_graph(self, layer, previous_state):
with layer_scope(self):
if previous_state is None:
input_batch = tf.shape(layer.tensor)[0]
zero_state = tf.zeros([input_batch, self.n_units])
self.previous_state = tx.TensorLayer(zero_state, self.n_units)
if self.share_state_with is None:
kernel_linear = tx.Linear(layer,
self.n_units,
bias=True,
weight_init=self.init,
name="linear_kernel")
kernel_act = tx.Activation(kernel_linear, self.activation)
self.kernel = tx.Compose([kernel_linear, kernel_act])
self.recurrent_kernel = tx.Linear(
self.previous_state,
self.n_units,
bias=False,
weight_init=self.recurrent_init,
name="recurrent_kernel")
else:
self.kernel = self.share_state_with.kernel.reuse_with(layer)
self.recurrent_kernel = self.share_state_with.recurrent_kernel.reuse_with(
self.previous_state)
# TODO this might be wrong, I might need to couple the activation: act(kernel + recurrent + bias)
# TODO it is wrong https://github.com/tensorflow/tensorflow/blob/r1.8/tensorflow/python/ops/rnn_cell_impl.py
# """Most basic RNN: output = new_state = act(W * input + U * state + B)."""
return self.kernel.tensor + self.recurrent_kernel.tensor
import tensorflow as tf import tensorx as tx from deepsign.models.nrp import RandomIndexTensor from deepsign.rp.ri import Generator, RandomIndex import numpy as np sess = tf.InteractiveSession() vocab_size = 8 k = 6 s = 2 emebd = 3 generator = Generator(k, s) ris = [generator.generate() for _ in range(vocab_size)] ri_tensor = RandomIndexTensor.from_ri_list(ris, k, s) ri_input = ri_tensor.gather([[0, 1, 0], [1, 2, 0]]) sp = ri_input.to_sparse_tensor() sp = tx.TensorLayer(sp, k) print(sp.tensor.eval()) embed = tx.Lookup(sp, seq_size=3, lookup_shape=[k, 3]) tf.global_variables_initializer().run() print(np.shape(embed.tensor.eval()))
batch_size = 2
generator = Generator(k, s)
print([vocab[w] for w in vocab.keys()])
ri_dict = {vocab[word]: generator.generate() for word in vocab.keys()}
tokens = [vocab[w] for w in tokens]
data_it = window_it(tokens, seq_size)
data_it = batch_it(data_it, batch_size)
vocab_tensor = [ri_dict[i] for i in range(len(vocab))]
sp_ri = deepsign.data.transform.ris_to_sp_tensor_value(vocab_tensor, dim=k)
inputs = tx.Input(n_units=2)
ri_inputs = tx.gather_sparse(sp_ri, inputs.tensor)
ri_inputs = tx.TensorLayer(ri_inputs, k)
embed = tx.Lookup(ri_inputs, seq_size, [k, embed_dim])
# logits: take the embeddings and get the features for all random indexes
ri_layer = tx.TensorLayer(sp_ri, n_units=k)
logits = tx.Linear(input_layer=ri_layer,
n_units=embed_dim,
shared_weights=embed.weights,
bias=True)
single_input = tx.Input(1)
ri_input = tx.TensorLayer(tx.gather_sparse(sp_ri, single_input.tensor), k)
logit = logits.reuse_with(ri_input)
from tensorflow.contrib.compiler import xla
jit_scope = tf.contrib.compiler.jit.experimental_jit_scope
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
input_size = 10000
var_size = 500
batch_size = 20
seq_size = 30
inputs = tf.constant(np.random.randint(0, 10, size=[batch_size, seq_size]), name="inputs")
targets = tf.constant(np.random.randint(0, 10, size=[batch_size * seq_size]), name="targets")
targets = tf.one_hot(targets, input_size)
inputs = tx.TensorLayer(inputs)
with jit_scope():
with tf.name_scope("scope1"):
lookup = tx.Lookup(inputs, seq_size=seq_size, lookup_shape=[input_size, var_size], name="lookup")
seq = lookup.permute_batch_time()
seq = tx.Reshape(seq, [-1, var_size], name="flatten")
mul1 = tx.Linear(seq, input_size, name="test_logits")
mul2 = tx.Linear(seq,
n_units=input_size,
shared_weights=lookup.weights,
transpose_weights=True,
name="shared_embeddings")
with tf.name_scope("scope2"):
mul1 = mul1.reuse_with(seq)
# *************************************
generator = Generator(k, s)
ris = [generator.generate() for _ in range(vocab_size)]
ri_tensor = ris_to_sp_tensor_value(ris, k)
ri_tensor = tf.convert_to_tensor_or_sparse_tensor(ri_tensor)
# *************************************
# DUMMY INPUT DATA
# *************************************
# batch of word sequence indices
ctx_size = 3
input_data = np.array([[0, 1, 2], [0, 2, 2], [1, 3, 5], [3, 0, 2]])
input_labels = tf.constant(np.array([[3], [1], [10], [25]], dtype=np.int64))
input_labels = tx.TensorLayer(input_labels, n_units=1)
input_layer = tx.TensorLayer(input_data, n_units=3, dtype=tf.int64)
ri_layer = tx.TensorLayer(ri_tensor, k)
ri_inputs = tx.gather_sparse(ri_layer.tensor, input_layer.tensor)
ri_inputs = tx.TensorLayer(ri_inputs, k)
lookup = tx.Lookup(ri_inputs,
ctx_size, [k, embed_size],
weight_init=tx.random_normal(0, 0.1),
name="lookup")
feature_predict = tx.Linear(lookup, embed_size, bias=True)
all_embeddings = tx.Linear(ri_layer,
embed_size,
shared_weights=lookup.weights,
lookup_shape=feature_shape)
# [batch x seq_size * feature_shape[1]]
# reshape to [batch x seq_size x feature_shape[1]]
lookup_to_seq = tf.reshape(lookup.tensor, [-1, seq_size, embed_dim])
# type of rnn cell
cell = tf.nn.rnn_cell.LSTMCell(num_units=n_hidden, state_is_tuple=True)
val, state = tf.nn.dynamic_rnn(cell, lookup_to_seq, dtype=tf.float32)
val = tf.transpose(val, [1, 0, 2])
# last = tf.gather(val, int(val.get_shape()[0]) - 1)
last = val[-1]
lstm_out = tx.TensorLayer(last, n_hidden)
logits = tx.Linear(lstm_out, vocab_size, bias=True)
out = tx.Activation(logits, tx.softmax)
labels = tx.dense_one_hot(loss_inputs.tensor, vocab_size)
loss = tf.reduce_mean(tx.categorical_cross_entropy(labels=labels, logits=logits.tensor))
# setup optimizer
optimizer = tx.AMSGrad(learning_rate=0.01)
model = tx.Model(run_inputs=in_layer, run_outputs=out,
train_inputs=in_layer, train_outputs=out,
train_in_loss=loss_inputs, train_out_loss=loss,
eval_out_score=loss, eval_in_score=loss_inputs)
print(model.feedable_train())
def __init__(self,
ctx_size,
vocab_size,
k_dim,
s_active,
ri_tensor,
embed_dim,
h_dim,
embed_init=tx.random_uniform(minval=-0.01, maxval=0.01),
logit_init=tx.random_uniform(minval=-0.01, maxval=0.01),
num_h=1,
h_activation=tx.relu,
h_init=tx.he_normal_init,
use_dropout=False,
embed_dropout=False,
keep_prob=0.95,
l2_loss=False,
l2_loss_coef=1e-5,
f_init=tx.random_uniform(minval=-0.01, maxval=0.01),
embed_share=True,
logit_bias=False,
use_nce=False,
nce_samples=100,
noise_level=0.1):
run_inputs = tx.Input(ctx_size, dtype=tf.int32)
loss_inputs = tx.Input(n_units=1, dtype=tf.int64)
eval_inputs = loss_inputs
if run_inputs.dtype != tf.int32 and run_inputs.dtype != tf.int64:
raise TypeError(
"Invalid dtype for input: expected int32 or int64, got {}".
format(run_inputs.dtype))
if num_h < 0:
raise ValueError("num hidden should be >= 0")
# ===============================================
# RUN GRAPH
# ===============================================
var_reg = []
with tf.name_scope("run"):
# RI ENCODING ===============================================
# convert ids to ris gather a set of random indexes based on the ids in a sequence
# ri_layer = tx.TensorLayer(ri_tensor, n_units=k_dim)
# ri_inputs = tx.gather_sparse(ri_layer.tensor, run_inputs.tensor)
# ri_inputs = tx.TensorLayer(ri_inputs, n_units=k_dim)
with tf.name_scope("ri_encode"):
if isinstance(ri_tensor, RandomIndexTensor):
ri_tensor = ri_tensor
ri_layer = tx.TensorLayer(ri_tensor.to_sparse_tensor(),
k_dim,
shape=[vocab_size, k_dim])
ri_inputs = ri_tensor.gather(run_inputs.tensor)
ri_inputs = ri_inputs.to_sparse_tensor()
ri_inputs = tx.TensorLayer(
ri_inputs,
k_dim,
shape=[ri_inputs.get_shape()[0], k_dim])
# ri_tensor is a sparse tensor
else:
raise TypeError(
"please supply RandomIndexTensor instead of sparse Tensor"
)
# ri_layer = tx.TensorLayer(ri_tensor, k_dim)
# ri_inputs = tx.gather_sparse(ri_layer.tensor, run_inputs.tensor)
# ri_inputs = tx.TensorLayer(ri_inputs, k_dim)
feature_lookup = tx.Lookup(ri_inputs,
ctx_size, [k_dim, embed_dim],
embed_init,
name="lookup")
self.embeddings = feature_lookup
var_reg.append(feature_lookup.weights)
feature_lookup = feature_lookup.as_concat()
# ===========================================================
last_layer = feature_lookup
h_layers = []
for i in range(num_h):
h_i = tx.Linear(last_layer,
h_dim,
h_init,
bias=True,
name="h_{i}_linear".format(i=i))
h_a = tx.Activation(h_i, h_activation)
h = tx.Compose(h_i, h_a, name="h_{i}".format(i=i))
h_layers.append(h)
last_layer = h
var_reg.append(h_i.weights)
self.h_layers = h_layers
# feature prediction for Energy-Based Model
f_prediction = tx.Linear(last_layer,
embed_dim,
f_init,
bias=True,
name="f_predict")
var_reg.append(f_prediction.weights)
# RI DECODING ===============================================
# Shared Embeddings
if embed_share:
shared_weights = feature_lookup.weights if embed_share else None
logit_init = logit_init if not embed_share else None
# ri_dense = tx.ToDense(ri_layer)
all_embeddings = tx.Linear(ri_layer,
embed_dim,
logit_init,
shared_weights,
name="all_features",
bias=False)
# dot product of f_predicted . all_embeddings with bias for each target word
run_logits = tx.Linear(f_prediction,
vocab_size,
shared_weights=all_embeddings.tensor,
transpose_weights=True,
bias=logit_bias,
name="logits")
else:
run_logits = tx.Linear(f_prediction,
vocab_size,
bias=logit_bias,
name="logits")
if not embed_share:
var_reg.append(run_logits.weights)
# ===========================================================
embed_prob = tx.Activation(run_logits,
tx.softmax,
name="run_output")
# ===============================================
# TRAIN GRAPH
# ===============================================
with tf.name_scope("train"):
if use_dropout and embed_dropout:
feature_lookup = feature_lookup.reuse_with(ri_inputs)
last_layer = tx.Dropout(feature_lookup, probability=keep_prob)
else:
last_layer = feature_lookup
# add dropout between each layer
for layer in h_layers:
h = layer.reuse_with(last_layer)
if use_dropout:
h = tx.Dropout(h, probability=keep_prob)
last_layer = h
f_prediction = f_prediction.reuse_with(last_layer)
train_logits = run_logits.reuse_with(f_prediction,
name="train_logits")
train_embed_prob = tx.Activation(train_logits,
tx.softmax,
name="train_output")
if use_nce:
# labels
labels = loss_inputs.tensor
# convert labels to random indices
def labels_to_ri(x):
random_index_tensor = ri_tensor.gather(x)
sp_features = random_index_tensor.to_sparse_tensor()
return sp_features
model_prediction = f_prediction.tensor
train_loss = tx.sparse_cnce_loss(
label_features=labels,
model_prediction=model_prediction,
weights=feature_lookup.weights,
noise_ratio=noise_level,
num_samples=nce_samples,
labels_to_sparse_features=labels_to_ri)
else:
one_hot = tx.dense_one_hot(column_indices=loss_inputs.tensor,
num_cols=vocab_size)
train_loss = tx.categorical_cross_entropy(
one_hot, train_logits.tensor)
train_loss = tf.reduce_mean(train_loss)
if l2_loss:
losses = [tf.nn.l2_loss(var) for var in var_reg]
train_loss = train_loss + l2_loss_coef * tf.add_n(losses)
# ===============================================
# EVAL GRAPH
# ===============================================
with tf.name_scope("eval"):
one_hot = tx.dense_one_hot(column_indices=eval_inputs.tensor,
num_cols=vocab_size)
eval_loss = tx.categorical_cross_entropy(one_hot,
run_logits.tensor)
eval_loss = tf.reduce_mean(eval_loss)
# BUILD MODEL
super().__init__(run_inputs=run_inputs,
run_outputs=embed_prob,
train_inputs=run_inputs,
train_outputs=train_embed_prob,
eval_inputs=run_inputs,
eval_outputs=embed_prob,
train_out_loss=train_loss,
train_in_loss=loss_inputs,
eval_out_score=eval_loss,
eval_in_score=eval_inputs)
out2 = out2.stack()
""" ********************************************************************************************
"""
ta_output = tf.TensorArray(dtype=tf.float32,
size=seq_size,
tensor_array_name="output_tensors")
# I cant accumulate objects inside while loop so I cant use the following in graph mode
# cells = []
# cells.append(tx.RNNCell(x0, n_units=H, previous_cell=None))
# use cell[0]
# also the states are wrong so I must use a TensorArray to pass the states
x0 = ta_input.read(0)
x0 = tx.TensorLayer(x0)
cell = tx.RNNCell(x0, M)
ta_output = ta_output.write(0, cell.tensor)
init_vars = (1, ta_output, cell.state)
cond_rnn = lambda i, *_: tf.less(i, seq_size)
print("creating rnn body")
def rnn_unroll(i, y, state):
xt = ta_input.read(i)
xt = tx.TensorLayer(xt)
c = cell.reuse_with(xt, previous_state=state)
y = y.write(i, c.tensor)
return i + 1, y, c.state
def rnn_unroll(i, y, state):
xt = ta_input.read(i)
xt = tx.TensorLayer(xt)
c = cell.reuse_with(xt, previous_state=state)
y = y.write(i, c.tensor)
return i + 1, y, c.state
unique=True,
range_max=vocab_size,
seed=None)
sampled, true_expected_count, sampled_expected_count = (
tf.stop_gradient(s) for s in sampled_values)
sampled = tf.cast(sampled, tf.int64)
all_ids = tf.concat([labels_flat, sampled], 0)
all_ris = tx.gather_sparse(ri_tensor, all_ids)
# Retrieve the true weights and the logits of the sampled weights.
# weights shape is [num_classes, dim]
ri_layer = tx.TensorLayer(ri_tensor, k)
l = tx.Linear(ri_layer, embed_size, weight_init=tx.random_normal(0, 1), bias=True)
weights = l.weights
sp_values = all_ris
sp_indices = tx.sparse_indices(sp_values)
all_w = tf.nn.embedding_lookup_sparse(
weights, sp_indices, sp_values, combiner="sum")
tf.global_variables_initializer().run()
print("labels flat: ", labels_flat.eval())
print("all labels: ", all_ids.eval())
print("ri_tensor \n", all_ris.eval())
W: width of the image
C: number of channels of the image (ex: 3 for RGB, 1 for grayscale...)
since we're processing vector representations
N == batch_size == 2
H == 1 (we're working with vectors)
W == input_dim == 2
C == channels == 1
NWC, channels are last we only use one
"""
x = tf.reshape(x_concat, [batch_size, seq_size, input_dim])
x_layer = tx.TensorLayer(x, input_dim)
print(x.eval())
print(x_layer.tensor)
filters = tf.get_variable("filters",
shape=filter_shape,
dtype=tf.float32,
initializer=tf.initializers.random_uniform(-1, 1))
filters = tf.ones(filter_shape)
c_layer = tx.Conv1D(x_layer,
num_filters,
kernel_size,
shared_filters=filters,
def __init__(self,
ctx_size,
vocab_size,
k_dim,
ri_tensor: RandomIndexTensor,
embed_dim,
embed_init=tx.random_uniform(minval=-0.01, maxval=0.01),
x_to_f_init=tx.random_uniform(minval=-0.01, maxval=0.01),
logit_init=tx.random_uniform(minval=-0.01, maxval=0.01),
embed_share=True,
logit_bias=False,
use_gate=True,
use_hidden=False,
h_dim=100,
h_activation=tx.elu,
h_init=tx.he_normal_init(),
h_to_f_init=tx.random_uniform(minval=-0.01, maxval=0.01),
use_dropout=True,
embed_dropout=False,
keep_prob=0.95,
l2_loss=False,
l2_loss_coef=1e-5):
# GRAPH INPUTS
run_inputs = tx.Input(ctx_size, dtype=tf.int32, name="input")
loss_inputs = tx.Input(n_units=1, dtype=tf.int32, name="target")
eval_inputs = loss_inputs
# RUN GRAPH =====================================================
var_reg = []
with tf.name_scope("run"):
# RI ENCODING ===============================================
# convert ids to ris gather a set of random indexes based on the ids in a sequence
# ri_layer = tx.TensorLayer(ri_tensor, n_units=k_dim)
# ri_inputs = tx.gather_sparse(ri_layer.tensor, run_inputs.tensor)
with tf.name_scope("ri_encode"):
# used to compute logits
if isinstance(ri_tensor, RandomIndexTensor):
ri_layer = tx.TensorLayer(ri_tensor.to_sparse_tensor(),
k_dim)
ri_inputs = ri_tensor.gather(run_inputs.tensor)
ri_inputs = ri_inputs.to_sparse_tensor()
ri_inputs = tx.TensorLayer(ri_inputs, k_dim)
else:
ri_layer = tx.TensorLayer(ri_tensor, k_dim)
ri_inputs = tx.gather_sparse(ri_layer.tensor,
run_inputs.tensor)
ri_inputs = tx.TensorLayer(ri_inputs, k_dim)
# use those sparse indexes to lookup a set of features based on the ri values
feature_lookup = tx.Lookup(ri_inputs,
ctx_size, [k_dim, embed_dim],
embed_init,
name="lookup")
var_reg.append(feature_lookup.weights)
feature_lookup = feature_lookup.as_concat()
# ===========================================================
if use_gate or use_hidden:
hl = tx.Linear(feature_lookup,
h_dim,
h_init,
bias=True,
name="h_linear")
ha = tx.Activation(hl, h_activation, name="h_activation")
h = tx.Compose(hl, ha, name="hidden")
var_reg.append(hl.weights)
features = feature_lookup
if use_gate:
features = tx.Gate(features, ctx_size, gate_input=h)
gate = features
var_reg.append(features.gate_weights)
x_to_f = tx.Linear(features,
embed_dim,
x_to_f_init,
bias=True,
name="x_to_f")
var_reg.append(x_to_f.weights)
f_prediction = x_to_f
if use_hidden:
h_to_f = tx.Linear(h,
embed_dim,
h_to_f_init,
bias=True,
name="h_to_f")
var_reg.append(h_to_f.weights)
f_prediction = tx.Add(x_to_f, h_to_f, name="f_predicted")
# RI DECODING ===============================================
shared_weights = feature_lookup.weights if embed_share else None
logit_init = logit_init if not embed_share else None
# embedding feature vectors for all words: shape [vocab_size, embed_dim]
# later, for NCE we don't need to get all the features
all_embeddings = tx.Linear(ri_layer,
embed_dim,
logit_init,
shared_weights,
name="logits",
bias=False)
# dot product of f_predicted . all_embeddings with bias for each target word
run_logits = tx.Linear(f_prediction,
n_units=vocab_size,
shared_weights=all_embeddings.tensor,
transpose_weights=True,
bias=logit_bias)
if not embed_share:
var_reg.append(all_embeddings.weights)
# ===========================================================
run_embed_prob = tx.Activation(run_logits, tx.softmax)
# TRAIN GRAPH ===================================================
with tf.name_scope("train"):
if use_dropout and embed_dropout:
feature_lookup = feature_lookup.reuse_with(ri_inputs)
features = tx.Dropout(feature_lookup, probability=keep_prob)
else:
features = feature_lookup
if use_gate or use_hidden:
if use_dropout:
h = h.reuse_with(features)
h = tx.Dropout(h, probability=keep_prob)
if use_gate:
features = gate.reuse_with(features, gate_input=h)
f_prediction = x_to_f.reuse_with(features)
if use_hidden:
h_to_f = h_to_f.reuse_with(h)
if use_dropout:
h_to_f = tx.Dropout(h_to_f, probability=keep_prob)
f_prediction = tx.Add(f_prediction, h_to_f)
else:
f_prediction = f_prediction.reuse_with(features)
# we already define all_embeddings from which these logits are computed before so this should be ok
train_logits = run_logits.reuse_with(f_prediction)
train_embed_prob = tx.Activation(train_logits,
tx.softmax,
name="train_output")
one_hot = tx.dense_one_hot(column_indices=loss_inputs.tensor,
num_cols=vocab_size)
train_loss = tx.categorical_cross_entropy(one_hot,
train_logits.tensor)
train_loss = tf.reduce_mean(train_loss)
if l2_loss:
losses = [tf.nn.l2_loss(var) for var in var_reg]
train_loss = train_loss + l2_loss_coef * tf.add_n(losses)
# EVAL GRAPH ===============================================
with tf.name_scope("eval"):
one_hot = tx.dense_one_hot(column_indices=eval_inputs.tensor,
num_cols=vocab_size)
eval_loss = tx.categorical_cross_entropy(one_hot,
run_logits.tensor)
eval_loss = tf.reduce_mean(eval_loss)
# SETUP MODEL CONTAINER ====================================
super().__init__(run_inputs=run_inputs,
run_outputs=run_embed_prob,
train_inputs=run_inputs,
train_outputs=train_embed_prob,
eval_inputs=run_inputs,
eval_outputs=run_embed_prob,
train_out_loss=train_loss,
train_in_loss=loss_inputs,
eval_out_score=eval_loss,
eval_in_score=eval_inputs)
embed_size = 4
generator = Generator(k, s)
ris = [generator.generate() for _ in range(vocab_size)]
ri_tensor = RandomIndexTensor.from_ri_list(ris, k, s)
sp_values = ri_tensor.gather(flat_labels).to_sparse_tensor()
sp_indices = tx.sparse_indices(sp_values)
print(sp_values.get_shape())
print(tensor_util.constant_value_as_shape(sp_values.dense_shape))
print(tensor_util.constant_value(sp_values.dense_shape))
print(sp_values.dense_shape[-1].eval())
print(tf.shape(sp_values).eval())
lookup = tx.Lookup(tx.TensorLayer(sp_values),
seq_size=1,
lookup_shape=[k, embed_size])
linear = tx.Linear(tx.TensorLayer(sp_values),
n_units=k,
shared_weights=lookup.weights)
w = embedding_lookup_sparse(params=lookup.weights,
sp_ids=sp_indices,
sp_weights=sp_values,
combiner="sum",
partition_strategy="mod")
tf.global_variables_initializer().run()
def _sampled_logits_from_parametric_noise(ri_tensors,
k_dim,
weights,
labels,
inputs,
input_dim,
num_true=1,
partition_strategy="mod",
name=None):
if isinstance(weights, variables.PartitionedVariable):
weights = list(weights)
if not isinstance(weights, list):
weights = [weights]
with ops.name_scope(name, "compute_sampled_logits",
weights + [inputs, labels]):
if labels.dtype != dtypes.int64:
labels = math_ops.cast(labels, dtypes.int64)
labels_flat = array_ops.reshape(labels, [-1])
# true_ris
true_ris = tx.gather_sparse(sp_tensor=ri_tensors, ids=labels_flat)
true_w = embedding_lookup_sparse(params=weights,
sp_ids=tx.sparse_indices(true_ris),
sp_weights=true_ris,
combiner="sum",
partition_strategy=partition_strategy)
label_layer = tx.TensorLayer(true_w, input_dim)
noise_fn = tx.FC(label_layer, 512, activation=tx.relu)
noise_fn_sp = tx.ToSparse(noise_fn)
noise_ris = tx.Linear(noise_fn_sp, k_dim, weight_init=tx.glorot_uniform(), bias=True)
noise_ris_sp = tx.ToSparse(noise_ris)
noise_w = embedding_lookup_sparse(params=weights,
sp_ids=tx.sparse_indices(noise_ris_sp.tensor),
sp_weights=noise_ris_sp.tensor,
combiner="sum",
partition_strategy=partition_strategy)
noise_logits = math_ops.matmul(inputs, noise_w, transpose_b=True)
dim = array_ops.shape(true_w)[1:2]
new_true_w_shape = array_ops.concat([[-1, num_true], dim], 0)
true_w_e = array_ops.reshape(true_w, new_true_w_shape)
row_wise_dots = math_ops.multiply(array_ops.expand_dims(inputs, 1),
true_w_e)
# We want the row-wise dot plus biases which yields a
# [batch_size, num_true] tensor of true_logits.
dots_as_matrix = array_ops.reshape(row_wise_dots,
array_ops.concat([[-1], dim], 0))
true_logits = array_ops.reshape(_sum_rows(dots_as_matrix), [-1, num_true])
# Construct output logits and labels. The true labels/logits start at col 0.
out_logits = array_ops.concat([true_logits, noise_logits], 1)
# true_logits is a float tensor, ones_like(true_logits) is a float
# tensor of ones. We then divide by num_true to ensure the per-example
# labels sum to 1.0, i.e. form a proper probability distribution.
out_labels = array_ops.concat([
array_ops.ones_like(true_logits) / num_true,
array_ops.zeros_like(noise_logits)
], 1)
# out_logits = out_logits * math_ops.exp(partition_const)
# out_logits = out_logits / (partition_const + 1)
return out_logits, out_labels
import tensorflow as tf
import tensorx as tx
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
n_features = 3
embed_size = 4
cell_units = 2
seq_size = 3
batch_size = 2
inputs = tx.TensorLayer(np.random.random([batch_size, seq_size]),
n_units=seq_size,
dtype=tf.int32)
lookup = tx.Lookup(inputs,
seq_size=seq_size,
lookup_shape=[n_features, embed_size])
seq = lookup.permute_batch_time()
# first step of a sequence
t1 = seq[0]
ks_cell = tf.keras.layers.LSTMCell(units=cell_units)
tf_cell = tf.nn.rnn_cell.LSTMCell(num_units=cell_units, state_is_tuple=True)
tx_cell = tx.LSTMCell(t1, n_units=cell_units)
kernel_w = [
tx_cell.w_i.weights, tx_cell.w_c.weights, tx_cell.w_f.weights,
tx_cell.w_o.weights
gate = tx.Linear(h, 2, bias=True)
gate = tx.Activation(gate, tx.sigmoid)
# lookup might output a sequence format with [batch,seq_size,m_dim]
# lookup_out = lookup.tensor
lookup_out = tf.reshape(lookup.tensor, [-1, seq_size, m_dim])
# reshape works anyway
gated_out = tf.reshape(lookup_out, [-1, seq_size, m_dim]) * tf.expand_dims(
gate.tensor, -1)
# gated_out = tf.reshape(gated_out, [-1, seq_size * m_dim])
# gated_out = tf.reshape(gated_out, [-1, lookup.n_units])
gated_out = tf.reshape(gated_out, tf.shape(lookup.tensor))
gated_out = tx.TensorLayer(gated_out, lookup.n_units)
# END GATING MECHANISM
y = tx.Linear(gated_out, m_dim, bias=True)
ss.run(tf.global_variables_initializer())
lookup_out = lookup.tensor.eval({inputs.placeholder: w})
assert (np.shape(lookup_out) == (3, 2 * m_dim))
print(np.shape(lookup_out))
gated_out = gated_out.tensor.eval({inputs.placeholder: w})
print(np.shape(gated_out))
gate_values = gate.tensor.eval({inputs.placeholder: w})