def __init__(self, args, infer=False):
'''these arguments appear in full in train.py'''
self.args = args
'''it seems this will never happen'''
if infer:
args.batch_size = 1
args.seq_length = 1
'''the types of models at our disposal'''
if args.model == 'rnn':
cell_fn = rnn.BasicRNNCell
elif args.model == 'gru':
cell_fn = rnn.GRUCell
elif args.model == 'lstm':
cell_fn = rnn.BasicLSTMCell
else:
raise Exception("model type not supported: {}".format(args.model))
'''this is a placeholder for dropout, defaults to 0 for computing f(x)'''
self.dropout = tf.placeholder_with_default(0., shape=())
'''the structure of the cell is formed here'''
cells = []
for _ in range(args.num_layers):
cell = cell_fn(args.rnn_size)
cell = rnn.DropoutWrapper(cell, output_keep_prob=1 - self.dropout)
cells.append(cell)
self.cell = cell = rnn.MultiRNNCell(cells)
'''the model object includes train data, test data if specified, and some batch/epoch pointers'''
self.input_data = tf.placeholder(tf.int32,
[args.batch_size, args.seq_length])
self.targets = tf.placeholder(tf.int32,
[args.batch_size, args.seq_length])
self.initial_state = cell.zero_state(args.batch_size, tf.float32)
self.batch_pointer = tf.Variable(0,
name="batch_pointer",
trainable=False,
dtype=tf.int32)
self.inc_batch_pointer_op = tf.assign(self.batch_pointer,
self.batch_pointer + 1)
self.epoch_pointer = tf.Variable(0,
name="epoch_pointer",
trainable=False)
self.batch_time = tf.Variable(0.0, name="batch_time", trainable=False)
self.test_x = tf.placeholder(tf.int32,
shape=[args.batch_size, args.seq_length])
self.test_y = tf.placeholder(tf.int32,
[args.batch_size, args.seq_length])
'''i never figured out what this does'''
tf.summary.scalar("time_batch", self.batch_time)
def variable_summaries(var):
"""Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
#with tf.name_scope('stddev'):
# stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
#tf.summary.scalar('stddev', stddev)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
#tf.summary.histogram('histogram', var)
'''begin defining model variables'''
with tf.variable_scope('rnnlm'):
'''the get_variable is an initializer: here we get weights, then biases'''
softmax_w = tf.get_variable(
"softmax_w", [args.rnn_size, args.vocab_size],
initializer=tf.truncated_normal_initializer(mean=0.,
stddev=.1,
seed=2018,
dtype=tf.float32))
variable_summaries(softmax_w)
softmax_b = tf.get_variable("softmax_b", [args.vocab_size],
initializer=tf.constant_initializer(
np.repeat(0., args.vocab_size),
tf.float32, args.vocab_size))
variable_summaries(softmax_b)
with tf.device("/cpu:0"):
'''W will be the word embeddings'''
self.W = tf.Variable(tf.constant(
0.0, shape=[args.vocab_size, args.embedding_dim]),
name="W")
self.embedding_placeholder = tf.placeholder(
tf.float32, [args.vocab_size, args.embedding_dim])
self.embedding_init = self.W.assign(self.embedding_placeholder)
'''the data to input to the model for some computation'''
inputs = tf.split(
tf.nn.embedding_lookup(self.W, self.input_data),
args.seq_length, 1)
inputs = [tf.squeeze(input_, [1]) for input_ in inputs]
test_inputs = tf.split(
tf.nn.embedding_lookup(self.W, self.test_x),
args.seq_length, 1)
test_inputs = [
tf.squeeze(test_input_, [1]) for test_input_ in test_inputs
]
'''im not 100% on this one, but it never gets used'''
def loop(prev, _):
prev = tf.matmul(prev, softmax_w) + softmax_b
print(tf.argmax(prev, 1))
prev_symbol = tf.stop_gradient(tf.argmax(prev, 1))
print(prev_symbol)
return tf.nn.embedding_lookup(embedding, prev_symbol)
'''the model output, logits, probability distbution, and loss'''
outputs, last_state = legacy_seq2seq.rnn_decoder(
inputs,
self.initial_state,
cell,
loop_function=loop if infer else None,
scope='rnnlm')
output = tf.reshape(tf.concat(outputs, 1), [-1, args.rnn_size])
self.logits = tf.matmul(output, softmax_w) + softmax_b
self.temp = tf.placeholder_with_default(1., shape=())
self.temped_logits = self.logits / self.temp
self.probs = tf.nn.softmax(self.temped_logits)
loss = legacy_seq2seq.sequence_loss_by_example(
[self.logits], [tf.reshape(self.targets, [-1])],
[tf.ones([args.batch_size * args.seq_length])], args.vocab_size)
self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length
'''the test output, logits, and loss'''
test_outputs, test_last_state = legacy_seq2seq.rnn_decoder(
test_inputs,
self.initial_state,
cell,
loop_function=loop if infer else None,
scope='rnnlm')
test_output = tf.reshape(tf.concat(test_outputs, 1),
[-1, args.rnn_size])
self.test_logits = tf.matmul(test_output, softmax_w) + softmax_b
self.test_probs = tf.nn.softmax(self.test_logits)
test_loss = legacy_seq2seq.sequence_loss_by_example(
[self.test_logits], [tf.reshape(self.test_y, [-1])],
[tf.ones([self.test_y.shape[0]])], args.vocab_size)
self.test_cost = tf.reduce_sum(
test_loss) / args.batch_size / args.seq_length
tf.summary.scalar("cost", self.cost)
'''for retrieval of the final states'''
self.final_state = last_state
self.test_final_state = test_last_state
'''the optimizer'''
self.lr = tf.Variable(0.0, trainable=False)
optimizer = tf.train.AdamOptimizer(self.lr)
'''so this was the really hacky way i got the embeddings to be trainable on demand: two channels for optimization. turn whichever you wish'''
self.tvars = tf.trainable_variables()
self.tvars_no_W = [
var for var in tf.trainable_variables() if "W:0" not in var.name
]
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, self.tvars),
args.grad_clip)
grads_no_W, _ = tf.clip_by_global_norm(
tf.gradients(self.cost, self.tvars_no_W), args.grad_clip)
'''running this updates the parameters'''
self.train_op = optimizer.apply_gradients(zip(grads, self.tvars))
self.train_op_no_W = optimizer.apply_gradients(
zip(grads_no_W, self.tvars_no_W))
X = tf.placeholder(tf.int32, [None, sequence_length])
Y = tf.placeholder(tf.int32, [None, sequence_length])
# One-hot encoding
X_one_hot = tf.one_hot(X, num_classes)
print(X_one_hot) # check out the shape
# Make a lstm cell with hidden_size (each unit output vector size)
def lstm_cell():
cell = rnn.BasicLSTMCell(hidden_size, state_is_tuple=True)
return cell
multi_cells = rnn.MultiRNNCell([lstm_cell() for _ in range(2)],
state_is_tuple=True)
# outputs: unfolding size x hidden size, state = hidden size
outputs, _states = tf.nn.dynamic_rnn(multi_cells, X_one_hot, dtype=tf.float32)
# FC layer
X_for_fc = tf.reshape(outputs, [-1, hidden_size])
outputs = tf.contrib.layers.fully_connected(X_for_fc,
num_classes,
activation_fn=None)
# reshape out for sequence_loss
outputs = tf.reshape(outputs, [batch_size, sequence_length, num_classes])
# All weights are 1 (equal weights)
weights = tf.ones([batch_size, sequence_length])
with tf.device('/gpu:0'):
input_data = tf.placeholder(tf.int32, shape=[batch_size, num_steps])
target = tf.placeholder(tf.int32, shape=[batch_size, num_steps])
keep_prob = tf.placeholder(tf.float32)
embedding = tf.get_variable("embedding", [word_vocab_size, rnn_size])
inputs = tf.nn.embedding_lookup(embedding, input_data)
def rnn_cell():
return tf.contrib.rnn.DropoutWrapper(rnn.BasicLSTMCell(
num_hidden_units, reuse=False),
output_keep_prob=keep_prob,
variational_recurrent=True,
dtype=tf.float32)
cells = rnn.MultiRNNCell(
[rnn_cell() for _ in range(num_hidden_layers)])
rnn_initial_state = cells.zero_state(batch_size, dtype=tf.float32)
print(input_data.get_shape().as_list())
outputs, final_state = tf.nn.dynamic_rnn(
cells, inputs, initial_state=rnn_initial_state, dtype=tf.float32)
outputs = tf.reshape(tf.concat(outputs, 1), [-1, rnn_size])
softmax_w = tf.get_variable("softmax_w", [rnn_size, word_vocab_size])
softmax_b = tf.get_variable("softmax_b", [word_vocab_size])
logits = tf.matmul(outputs, softmax_w) + softmax_b
logits = tf.reshape(logits, [batch_size, num_steps, word_vocab_size])
loss = tf.contrib.seq2seq.sequence_loss(logits,
target,
tf.ones(
def network(self):
"""
RNN 网络搭建
:return:
"""
# 1. embedding layer
with tf.name_scope('embedding'):
if self.embedding_mat is None:
self.Embedding = tf.Variable(tf.random_uniform([self.vocab_size, self.embedding_dims],
-1., 1.), name='Embedding')
self.embedded_chars = tf.nn.embedding_lookup(self.Embedding, self.input_x)
# 2. RNN hidden layer
with tf.name_scope('rnn'):
if self.cell.startswith("bi"):
cell_fw, cell_bw = self.bi_dir_rnn()
if self.num_layer > 1:
cell_fw = rnn.MultiRNNCell([cell_fw] * self.num_layer, state_is_tuple=True)
cell_bw = rnn.MultiRNNCell([cell_bw] * self.num_layer, state_is_tuple=True)
outputs, _ = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, self.embedded_chars,
dtype=tf.float32)
# 将双向的LSTM 输出拼接，得到[None, time_step, hidden_dims * 2]
outputs = tf.concat(outputs, axis=2)
else:
cells = self.witch_cell()
if self.num_layer > 1:
cells = rnn.MultiRNNCell([cells] * self.num_layer, state_is_tuple=True)
# outputs:[batch, timestep_size, hidden_size]
# state:[layer_num, 2, batch_size, hidden_size]
outputs, _ = tf.nn.dynamic_rnn(cells, self.embedded_chars, dtype=tf.float32)
# 取出最后一个状态的输出 [none, 1, hidden_dims * 2]
h_state = outputs[:, -1, :]
# 3. FC and softmax layer
with tf.name_scope('output'):
if self.cell.startswith('bi'):
self.W = tf.Variable(tf.truncated_normal([self.hidden_unit * 2, self.num_tags], stddev=0.1),
dtype=tf.float32, name='W')
else:
self.W = tf.Variable(tf.truncated_normal([self.hidden_unit, self.num_tags], stddev=0.1),
dtype=tf.float32, name='W')
self.b = tf.Variable(tf.constant(0.1, shape=[self.num_tags]), dtype=tf.float32, name='b')
# full coneection and softmax output
self.logits = tf.nn.softmax(tf.matmul(h_state, self.W) + self.b)
# 4. loss
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=self.input_y)
self.loss = tf.reduce_mean(cross_entropy)
# l2_loss = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()
# if 'bias' not in v.name]) * self.l2_reg_lambda
self.l2_loss += tf.nn.l2_loss(self.W)
self.l2_loss += tf.nn.l2_loss(self.b)
self.loss += self.l2_loss
# 5. accuracy
with tf.name_scope('accuracy'):
self.predicted = tf.equal(tf.argmax(self.logits, 1),
tf.arg_max(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(self.predicted, dtype=tf.float32))
with tf.name_scope('num_prediction'):
self.num_correct = tf.reduce_sum(tf.cast(self.predicted, dtype=tf.float32), name='num_correct')
def __init__(self, args, training=True):
self.args = args
if not training:
args.batch_size = 1
args.seq_length = 1
if args.model == 'rnn':
cell_fn = rnn.BasicRNNCell
elif args.model == 'gru':
cell_fn = rnn.GRUCell
elif args.model == 'lstm':
cell_fn = rnn.BasicLSTMCell
elif args.model == 'nas':
cell_fn = rnn.NASCell
else:
raise Exception("model type not supported: {}".format(args.model))
cells = []
for _ in range(args.num_layers):
cell = cell_fn(args.rnn_size)
if training and (args.output_keep_prob < 1.0 or args.input_keep_prob < 1.0):
cell = rnn.DropoutWrapper(cell,
input_keep_prob=args.input_keep_prob,
output_keep_prob=args.output_keep_prob)
cells.append(cell)
self.cell = cell = rnn.MultiRNNCell(cells, state_is_tuple=True)
self.input_data = tf.placeholder(
tf.int32, [args.batch_size, args.seq_length])
self.targets = tf.placeholder(
tf.int32, [args.batch_size, args.seq_length])
self.initial_state = cell.zero_state(args.batch_size, tf.float32)
with tf.variable_scope('rnnlm'):
softmax_w = tf.get_variable("softmax_w",
[args.rnn_size, args.vocab_size])
softmax_b = tf.get_variable("softmax_b", [args.vocab_size])
embedding = tf.get_variable("embedding", [args.vocab_size, args.rnn_size])
inputs = tf.nn.embedding_lookup(embedding, self.input_data)
# dropout beta testing: double check which one should affect next line
if training and args.output_keep_prob:
inputs = tf.nn.dropout(inputs, args.output_keep_prob)
inputs = tf.split(inputs, args.seq_length, 1)
inputs = [tf.squeeze(input_, [1]) for input_ in inputs]
def loop(prev, _):
prev = tf.matmul(prev, softmax_w) + softmax_b
prev_symbol = tf.stop_gradient(tf.argmax(prev, 1))
return tf.nn.embedding_lookup(embedding, prev_symbol)
outputs, last_state = legacy_seq2seq.rnn_decoder(inputs, self.initial_state, cell, loop_function=loop if not training else None, scope='rnnlm')
output = tf.reshape(tf.concat(outputs, 1), [-1, args.rnn_size])
self.logits = tf.matmul(output, softmax_w) + softmax_b
self.probs = tf.nn.softmax(self.logits)
loss = legacy_seq2seq.sequence_loss_by_example(
[self.logits],
[tf.reshape(self.targets, [-1])],
[tf.ones([args.batch_size * args.seq_length])])
with tf.name_scope('cost'):
self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length
self.final_state = last_state
self.lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),
args.grad_clip)
with tf.name_scope('optimizer'):
optimizer = tf.train.AdamOptimizer(self.lr)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))
# instrument tensorboard
tf.summary.histogram('logits', self.logits)
tf.summary.histogram('loss', loss)
tf.summary.scalar('train_loss', self.cost)
def rnn_estimator(x, y):
"""RNN estimator with target predictor function on top."""
x = input_op_fn(x)
if cell_type == 'rnn':
cell_fn = contrib_rnn.BasicRNNCell
elif cell_type == 'gru':
cell_fn = contrib_rnn.GRUCell
elif cell_type == 'lstm':
cell_fn = functools.partial(contrib_rnn.BasicLSTMCell,
state_is_tuple=False)
else:
raise ValueError(
'cell_type {} is not supported. '.format(cell_type))
# TODO(ipolosukhin): state_is_tuple=False is deprecated
if bidirectional:
# forward direction cell
fw_cell = lambda: cell_fn(rnn_size)
bw_cell = lambda: cell_fn(rnn_size)
# attach attention cells if specified
if attn_length is not None:
def attn_fw_cell():
return contrib_rnn.AttentionCellWrapper(
fw_cell(),
attn_length=attn_length,
attn_size=attn_size,
attn_vec_size=attn_vec_size,
state_is_tuple=False)
def attn_bw_cell():
return contrib_rnn.AttentionCellWrapper(
bw_cell(),
attn_length=attn_length,
attn_size=attn_size,
attn_vec_size=attn_vec_size,
state_is_tuple=False)
else:
attn_fw_cell = fw_cell
attn_bw_cell = bw_cell
rnn_fw_cell = contrib_rnn.MultiRNNCell(
[attn_fw_cell() for _ in range(num_layers)],
state_is_tuple=False)
# backward direction cell
rnn_bw_cell = contrib_rnn.MultiRNNCell(
[attn_bw_cell() for _ in range(num_layers)],
state_is_tuple=False)
# pylint: disable=unexpected-keyword-arg, no-value-for-parameter
_, encoding = bidirectional_rnn(rnn_fw_cell,
rnn_bw_cell,
x,
dtype=dtypes.float32,
sequence_length=sequence_length,
initial_state_fw=initial_state,
initial_state_bw=initial_state)
else:
rnn_cell = lambda: cell_fn(rnn_size)
if attn_length is not None:
def attn_rnn_cell():
return contrib_rnn.AttentionCellWrapper(
rnn_cell(),
attn_length=attn_length,
attn_size=attn_size,
attn_vec_size=attn_vec_size,
state_is_tuple=False)
else:
attn_rnn_cell = rnn_cell
cell = contrib_rnn.MultiRNNCell(
[attn_rnn_cell() for _ in range(num_layers)],
state_is_tuple=False)
_, encoding = contrib_rnn.static_rnn(
cell,
x,
dtype=dtypes.float32,
sequence_length=sequence_length,
initial_state=initial_state)
return target_predictor_fn(encoding, y)
eth_transaction_fee = tf.constant(0.001)
etc_transaction_fee = tf.constant(0.01)
SEQLEN = 30
BATCHSIZE = 200
INTERNALSIZE = 512
NLAYERS = 3
learning_rate = 0.001
# inputs/outputs
X = tf.placeholder(tf.float32, [None], name="X")
Y_ = tf.placeholder(tf.float32, [None], name="Y_")
# making the multirnn_gru_cell
Hin = tf.placeholder(tf.float32, [None, INTERNALSIZE * NLAYERS], name='Hin')
# [ BATCHSIZE, INTERNALSIZE * NLAYERS]
cells = [rnn.GRUCell(INTERNALSIZE) for _ in range(NLAYERS)]
multicell = rnn.MultiRNNCell(cells, state_is_tuple=False)
Yr, H = tf.nn.dynamic_rnn(multicell, X, dtype=tf.float32, initial_state=Hin)
H = tf.identity(H, name='H')
# checkpoints dir
if not os.path.exists("checkpoints"):
os.mkdir("checkpoints")
saver = tf.train.Saver(max_to_keep=1000)
# init
# initial zero input state
istate = np.zeros([BATCHSIZE, INTERNALSIZE * NLAYERS])
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
step = 0
def add_graph(self, noyear=False, feedforward=False):
"""
parameters:
noyear: a boolean, indicates whether year information is included as input to the model
feedforward: a boolean, indicates whether the model is a feedforward neural network or an LSTM
Creates a graph for the model. Generates placeholders for X_word, X_year, Y_label, and the embedding matrix. Creates
year embedding. Details model architecture. Calculates accuracy, log perplexity, and loss. Optimizes network based on loss.
"""
# Creates placeholders for LSTM
self.X_word = tf.placeholder(tf.int32, [None, MAX_SENT_LENGTH])
self.X_year = tf.placeholder(tf.int32, [None])
self.Y_label = tf.placeholder(tf.int32, [None, MAX_SENT_LENGTH])
self.embedding_matrix = tf.placeholder(tf.float32,
[MAX_THRESHOLD, EMBED_DIM])
# Looks up embeddings for each word
X_word = tf.nn.embedding_lookup(self.embedding_matrix, self.X_word)
# Creates year embedding
new_years = tf.subtract(self.X_year, START_YEAR)
unembedded_year = tf.tile(tf.expand_dims(new_years, axis=1),
[1, MAX_SENT_LENGTH])
self.year_embed_mat = tf.get_variable(
name="year_embed_mat",
shape=(NUM_YEAR, EMBED_DIM),
initializer=tf.contrib.layers.xavier_initializer())
embedded_year = tf.nn.embedding_lookup(self.year_embed_mat,
unembedded_year)
if noyear:
embedded_year = tf.zeros_like(embedded_year)
# Concatenates X_word and year embedding to get single combined input
X = tf.concat([X_word, embedded_year], axis=2)
if feedforward:
# Implements Feed-Forward
H = tf.layers.dense(inputs=X,
units=LAYERS[0],
activation=tf.nn.sigmoid)
else:
# Implements LSTM
rnn_layers = [rnn.LSTMCell(size) for size in LAYERS]
multi_rnn_cell = rnn.MultiRNNCell(rnn_layers)
H, _ = tf.nn.dynamic_rnn(cell=multi_rnn_cell,
inputs=X,
dtype=tf.float32)
# POS tags
self.Y = tf.contrib.layers.fully_connected(
inputs=H,
num_outputs=N_POS,
)
# Calculates accuracy
equal = tf.equal(tf.cast(tf.argmax(self.Y, axis=2), tf.int32),
tf.cast(self.Y_label, tf.int32))
self.acc = tf.reduce_mean(tf.cast(equal, tf.float32))
self.vec_acc = tf.reduce_mean(tf.cast(equal, tf.float32), axis=1)
# Calculates perplexity
mask = tf.cast(tf.one_hot(self.Y_label, N_POS), tf.float32)
p = tf.reduce_sum(tf.nn.softmax(self.Y) * mask, axis=2)
self.log_perp = -tf.reduce_sum(tf.log(p), axis=1) / MAX_SENT_LENGTH
self.perp = tf.exp(self.log_perp)
# Calculates loss
self.loss = tf.losses.sparse_softmax_cross_entropy(
labels=self.Y_label,
logits=self.Y,
)
# Sets train_step that uses AdamOptimizer to minimize loss
self.train_step = tf.train.AdamOptimizer(LR).minimize(self.loss)
weights={'in':tf.Variable(tf.random_normal([n_input,n_hidden])),
'out': tf.Variable(tf.random_normal([n_hidden,n_outputs]))}
biases={'in':tf.Variable(tf.constant(0.1,shape=[n_hidden,])),
'out': tf.Variable(tf.constant(0.1,shape=[n_outputs,]))}
X=train_x
Y=train_y
test=test_x
w_in=weights['in']
b_in=biases['in']
inputs=tf.reshape(x,[-1,n_input])
input_rnn=tf.matmul(inputs,w_in)+b_in
input_rnn=tf.reshape(input_rnn,[-1,time_step,n_hidden])
lstm_cells=[rnn.LSTMCell(n_hidden,forget_bias=1.0) for _ in range(n_layers)]
lstm=rnn.MultiRNNCell(lstm_cells)
outputs,states=tf.nn.dynamic_rnn(lstm,inputs=x,dtype=tf.float32,time_major=False)
outputs=tf.reshape(outputs,[-1,n_hidden])
w_out=weights['out']
b_out=biases['out']
pred=tf.matmul(outputs,w_out)+b_out
#损失函数
loss=tf.reduce_mean(tf.square(tf.reshape(pred,[-1])-tf.reshape(y, [-1])))
train_op=tf.train.AdamOptimizer(learning_rate).minimize(loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
#重复训练10000次
for i in range(1000):
step=0
start=0
end=start+batch_size
labels = tf.placeholder(tf.float32, shape=(None, 3), name='labels')
def feed_dict(X, asp, lx, y):
fd = {inputs[t]: X[t] for t in range(dm.max_seq_len)}
fd.update({asp_inputs: asp})
fd = {}
fd.update({labels: y})
return fd
cell = rnn.BasicLSTMCell(cell_num)
#cell = rnn.LSTMCell(cell_num, initializer=initializer)
#cell = rnn.DropoutWrapper(cell, output_keep_prob=dropout_keep_prob)
cells = [deepcopy(cell) for i in range(layer_num)]
cell = rnn.MultiRNNCell(cells)
with tf.name_scope('embedding'):
if dm.use_pretrained_embedding:
pre_trained_emb = embedding_frame.dropna().values.astype('float32')
pre_trained_embedding = tf.get_variable(
name="pre_trained_embedding",
shape=pre_trained_emb.shape,
initializer=tf.constant_initializer(pre_trained_emb),
trainable=True)
pad_embedding = tf.get_variable('pad_embedding',
(dm.start_idx, embedding_size),
dtype=tf.float32,
initializer=initializer)
embedding = tf.concat([pad_embedding, pre_trained_embedding],
axis=0,
def multi_cell():
return rnn.MultiRNNCell(
[single_cell() for _ in range(hyper.num_layer)])
l = tf.placeholder(tf.int32, [None])
weights = tf.Variable(tf.random_normal([n_hidden * 2, n_classes]))
biases = tf.Variable(tf.random_normal([n_classes]))
'''构建Graph'''
def GRU_cell():
cell = rnn.GRUCell(n_hidden, reuse=tf.get_variable_scope().reuse)
return rnn.DropoutWrapper(cell, output_keep_prob=keep_prob)
inputs = tf.transpose(x, [1, 0, 2])
inputs = tf.reshape(inputs, [-1, n_input])
inputs = tf.split(inputs, n_steps)
# ** 1.构建前向后向多层 LSTM
cell_fw = rnn.MultiRNNCell([GRU_cell() for _ in range(layer_num)],
state_is_tuple=True)
cell_bw = rnn.MultiRNNCell([GRU_cell() for _ in range(layer_num)],
state_is_tuple=True)
# ** 2.初始状态+
initial_state_fw = cell_fw.zero_state(batch_size, tf.float32)
initial_state_bw = cell_bw.zero_state(batch_size, tf.float32)
# ** 3.bi-lstm 计算（tf封装）
outputs, _, _ = rnn.static_bidirectional_rnn(cell_fw,
cell_bw,
inputs,
initial_state_fw=initial_state_fw,
initial_state_bw=initial_state_bw,
dtype=tf.float32,
sequence_length=l)
output = tf.reshape(tf.concat(outputs, 1), [-1, 2 * n_hidden])
logits = tf.matmul(output, weights) + biases
def main():
# **步骤1：RNN 的输入shape = (n_batch_size, timestep_size, n_input)
X = tf.placeholder(tf.float32, shape=(None, n_steps * n_input), name="X")
y = tf.placeholder(tf.float32, shape=(None, n_classes), name="y")
def lstm_cell(n_hidden, keep_prob):
# **步骤2：定义一层 LSTM_cell，只需要说明 n_hidden, 它会自动匹配输入的 X 的维度
cell = rnn.LSTMCell(num_units=n_hidden,
initializer=tf.random_uniform_initializer(-0.1,
0.1,
seed=2),
forget_bias=1.0,
state_is_tuple=True)
# **步骤3：添加 dropout layer, 一般只设置 output_keep_prob
cell = rnn.DropoutWrapper(cell=cell,
input_keep_prob=1.0,
output_keep_prob=keep_prob)
return cell
# **步骤4：调用 MultiRNNCell 来实现多层 LSTM
cells = [lstm_cell(n_hidden, keep_prob) for _ in range(n_layers)]
mlstm_cell = rnn.MultiRNNCell(cells, state_is_tuple=True)
# **步骤5：用全零来初始化state # 通过zero_state得到一个全0的初始状态
init_state = mlstm_cell.zero_state(n_batch_size, dtype=tf.float32)
# **步骤6：方法一，调用 dynamic_rnn() 来让我们构建好的网络运行起来
# ** 当 time_major==False 时， outputs.shape = [n_batch_size, timestep_size, n_hidden]
# ** 所以，可以取 h_state = outputs[:, -1, :] 作为最后输出
# ** state.shape = [layer_num, 2, n_batch_size, n_hidden],
# ** 或者，可以取 h_state = state[-1][1] 作为最后输出
# ** 最后输出维度是 [n_batch_size, n_hidden]
# outputs, state = tf.nn.dynamic_rnn(mlstm_cell, inputs=X, initial_state=init_state, time_major=False)
# h_state = outputs[:, -1, :] # 或者 h_state = state[-1][1]
# *************** 为了更好的理解 LSTM 工作原理，我们把上面 步骤6 中的函数自己来实现 ***************
# 通过查看文档你会发现， RNNCell 都提供了一个 __call__()函数（见最后附），我们可以用它来展开实现LSTM按时间步迭代。
# **步骤6：方法二，按时间步展开计算
outputs = list()
with tf.variable_scope('RNN'):
for timestep in range(n_steps):
if timestep > 0:
tf.get_variable_scope().reuse_variables()
# 这里的state保存了每一层 LSTM 的状态
cell_output, h1 = mlstm_cell.call(X, init_state)
outputs.append(cell_output)
h_state = outputs[-1]
# 上面 LSTM 部分的输出会是一个 [n_hidden] 的tensor，我们要分类的话，还需要接一个 softmax 层
# 首先定义 softmax 的连接权重矩阵和偏置
# out_W = tf.placeholder(tf.float32, [n_hidden, n_classes], name='out_Weights')
# out_bias = tf.placeholder(tf.float32, [n_classes], name='out_bias')
# 开始训练和测试
W = tf.Variable(tf.truncated_normal([n_hidden, n_classes]),
dtype=tf.float32)
bias = tf.Variable(tf.constant(0.1, shape=[n_classes]), dtype=tf.float32)
y_ = tf.nn.softmax(tf.matmul(h_state, W) + bias)
# 损失和评估函数
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=y_))
train_op = tf.train.AdamOptimizer(lr).minimize(cost)
correct_prediction = tf.equal(tf.argmax(y_, 1), tf.argmax(y, 1))
acc = tf.reduce_mean(tf.cast(correct_prediction, "float"))
with tf.Session() as session:
session.run(tf.global_variables_initializer())
for i in range(2000):
train_X, train_y = mnist.train.next_batch(n_batch_size)
session.run(train_op,
feed_dict={
X: train_X,
y: train_y,
keep_prob: 0.5
})
if (i + 1) % 200 == 0:
train_acc, train_loss = session.run([acc, cost],
feed_dict={
X: train_X,
y: train_y,
keep_prob: 1.0
})
# 已经迭代完成的 epoch 数: mnist.train.epochs_completed
print("Iter {}, step {}, loss {:6f}, train acc {}".format(
mnist.train.epochs_completed, (i + 1), train_loss,
train_acc))
print("\nevaluation model")
test_X = mnist.test.images[:n_batch_size]
test_y = mnist.test.labels[:n_batch_size]
# 计算测试数据的准确率
test_acc, test_loss = session.run([acc, cost],
feed_dict={
X: test_X,
y: test_y,
keep_prob: 1.0,
batch_size: n_batch_size
})
print("test acc {},test loss {}".format(test_acc, test_loss))
def add_rnn(layer_count, hidden_size, cell=rnn.BasicLSTMCell, activation=tf.tanh):
# hidden_size = 5，神经元序列
cells = [cell(hidden_size, activation=activation) for _ in range(layer_count)]
return rnn.MultiRNNCell(cells)
def __init__(self, params, training=True):
if not training:
params.batch_size = 1
params.seq_length = 1
cells = []
for _ in range(params.num_layers):
cell = rnn.BasicLSTMCell(params.rnn_size)
cells.append(cell)
self.cell = cell = rnn.MultiRNNCell(cells, state_is_tuple=True)
self.input_data = tf.placeholder(
tf.int32, [params.batch_size, params.seq_length])
self.targets = tf.placeholder(tf.int32,
[params.batch_size, params.seq_length])
self.initial_state = cell.zero_state(params.batch_size, tf.float32)
with tf.variable_scope('lstm_lm'):
softmax_w = tf.get_variable("softmax_w",
[params.rnn_size, params.vocab_size])
softmax_b = tf.get_variable("softmax_b", [params.vocab_size])
embedding = tf.get_variable("embedding",
[params.vocab_size, params.rnn_size])
inputs = tf.nn.embedding_lookup(embedding, self.input_data)
inputs = tf.split(inputs, params.seq_length, 1)
inputs = [tf.squeeze(input_, [1]) for input_ in inputs]
def loop(prev, _):
prev = tf.matmul(prev, softmax_w) + softmax_b
prev_symbol = tf.stop_gradient(tf.argmax(prev, 1))
return tf.nn.embedding_lookup(embedding, prev_symbol)
outputs, last_state = legacy_seq2seq.rnn_decoder(
inputs,
self.initial_state,
cell,
loop_function=loop if not training else None,
scope='lstm_lm')
output = tf.reshape(tf.concat(outputs, 1), [-1, params.rnn_size])
self.logits = tf.matmul(output, softmax_w) + softmax_b
self.probs = tf.nn.softmax(self.logits)
loss = legacy_seq2seq.sequence_loss_by_example(
[self.logits], [tf.reshape(self.targets, [-1])],
[tf.ones([params.batch_size * params.seq_length])])
self.cost = (tf.reduce_sum(loss) /
params.batch_size) / params.seq_length
with tf.name_scope('cost'):
self.cost = (tf.reduce_sum(loss) /
params.batch_size) / params.seq_length
self.final_state = last_state
self.lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),
params.grad_clip)
with tf.name_scope('optimizer'):
optimizer = tf.train.AdamOptimizer(self.lr)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))
tf.summary.histogram('logits', self.logits)
tf.summary.histogram('loss', loss)
tf.summary.scalar('train_loss', self.cost)
numInputs = 2
numOutputs = 1
timesteps = 1
resultSet = []
w = tf.Variable(tf.truncated_normal([numHidden2, numOutputs]))
b = tf.Variable(tf.random_normal([numOutputs]))
lstm = rnn.LSTMCell(numHidden, state_is_tuple=True)
lstm2 = rnn.LSTMCell(numHidden2, state_is_tuple=True)
lstm3 = rnn.LSTMCell(numHidden2, state_is_tuple=True)
cell = rnn.MultiRNNCell([lstm, lstm2])
def LSTM(X):
output, state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
output = tf.transpose(output, (1, 0, 2))
out = tf.tanh(tf.matmul(output[-1], w) + b)
return out
X = tf.placeholder(tf.float32, [None, timesteps, numInputs])
Y = tf.placeholder(tf.float32, [None, numOutputs])
def build_stacked_gru_model(
embedding_layer,
partial_sequence_length,
gru_hidden_sizes,
num_output_features,
bidirectional):
"""Predicts next production rule from partial sequence with stacked GRUs.
Args:
embedding_layer: Float32 tensor with shape
[batch_size, max_length, num_features]. Input to the model.
partial_sequence_length: Int32 tensor with shape [batch_size].
This tensor is used for sequence_length in tf.nn.dynamic_rnn().
gru_hidden_sizes: List of integers, number of units for each GRU layer.
num_output_features: Integer, the number of output features.
bidirectional: Boolean, whether to use bidirectional RNN.
Returns:
Float tensor with shape [batch_size, num_output_features]
"""
with tf.variable_scope('stacked_gru_model'):
gru_cells = [
tf.nn.rnn_cell.GRUCell(gru_hidden_size)
for gru_hidden_size in gru_hidden_sizes
]
forward_stacked_gru = contrib_rnn.MultiRNNCell(gru_cells)
if bidirectional:
gru_cells = [
tf.nn.rnn_cell.GRUCell(gru_hidden_size)
for gru_hidden_size in gru_hidden_sizes
]
backward_stacked_gru = contrib_rnn.MultiRNNCell(gru_cells)
_, final_states = tf.nn.bidirectional_dynamic_rnn(
cell_fw=forward_stacked_gru,
cell_bw=backward_stacked_gru,
inputs=embedding_layer,
sequence_length=partial_sequence_length,
dtype=embedding_layer.dtype,
time_major=False)
# final_states is a tuple of tuples:
# (
# (forward_gru_0, forward_gru_1, ...),
# (backward_gru_0, backward_gru_1, ...)
# )
# Flatten the tuple as
# (forward_gru_0, ..., backward_gru_0, ...)
final_states = final_states[0] + final_states[1]
else:
_, final_states = tf.nn.dynamic_rnn(
cell=forward_stacked_gru,
inputs=embedding_layer,
sequence_length=partial_sequence_length,
dtype=embedding_layer.dtype,
time_major=False)
concat_final_states = tf.concat(
final_states, axis=1, name='concatenate_gru_final_states')
logits = tf.layers.dense(
concat_final_states, num_output_features, name='logits')
return logits
def get_rnn_cell(hparams=None, mode=None):
"""Creates an RNN cell.
See :func:`~texar.core.default_rnn_cell_hparams` for all
hyperparameters and default values.
Args:
hparams (dict or HParams, optional): Cell hyperparameters. Missing
hyperparameters are set to default values.
mode (optional): A Tensor taking value in
:tf_main:`tf.estimator.ModeKeys <estimator/ModeKeys>`, including
`TRAIN`, `EVAL`, and `PREDICT`. If `None`, dropout will be
controlled by :func:`texar.global_mode`.
Returns:
A cell instance.
Raises:
ValueError: If hparams["num_layers"]>1 and hparams["type"] is a class
instance.
ValueError: The cell is not an
:tf_main:`RNNCell <contrib/rnn/RNNCell>` instance.
"""
if hparams is None or isinstance(hparams, dict):
hparams = HParams(hparams, default_rnn_cell_hparams())
d_hp = hparams["dropout"]
if d_hp["variational_recurrent"] and \
len(d_hp["input_size"]) != hparams["num_layers"]:
raise ValueError(
"If variational_recurrent=True, input_size must be a list of "
"num_layers(%d) integers. Got len(input_size)=%d." %
(hparams["num_layers"], len(d_hp["input_size"])))
cells = []
cell_kwargs = hparams["kwargs"].todict()
num_layers = hparams["num_layers"]
for layer_i in range(num_layers):
# Create the basic cell
cell_type = hparams["type"]
if not is_str(cell_type) and not isinstance(cell_type, type):
if num_layers > 1:
raise ValueError(
"If 'num_layers'>1, then 'type' must be a cell class or "
"its name/module path, rather than a cell instance.")
cell_modules = ['tensorflow.contrib.rnn', 'texar.custom']
cell = utils.check_or_get_instance(
cell_type, cell_kwargs, cell_modules, rnn.RNNCell)
# Optionally add dropout
if d_hp["input_keep_prob"] < 1.0 or \
d_hp["output_keep_prob"] < 1.0 or \
d_hp["state_keep_prob"] < 1.0:
vr_kwargs = {}
if d_hp["variational_recurrent"]:
vr_kwargs = {
"variational_recurrent": True,
"input_size": d_hp["input_size"][layer_i],
"dtype": tf.float32
}
input_keep_prob = switch_dropout(d_hp["input_keep_prob"],
mode)
output_keep_prob = switch_dropout(d_hp["output_keep_prob"],
mode)
state_keep_prob = switch_dropout(d_hp["state_keep_prob"],
mode)
cell = rnn.DropoutWrapper(
cell=cell,
input_keep_prob=input_keep_prob,
output_keep_prob=output_keep_prob,
state_keep_prob=state_keep_prob,
**vr_kwargs)
# Optionally add residual and highway connections
if layer_i > 0:
if hparams["residual"]:
cell = rnn.ResidualWrapper(cell)
if hparams["highway"]:
cell = rnn.HighwayWrapper(cell)
cells.append(cell)
if hparams["num_layers"] > 1:
cell = rnn.MultiRNNCell(cells)
else:
cell = cells[0]
return cell
def __init__(self, args, infer=False):
self.args = args
if infer:
args.batch_size = 1
args.seq_length = 1
if args.model == 'rnn':
cell_fn = rnn.BasicRNNCell
elif args.model == 'gru':
cell_fn = rnn.GRUCell
elif args.model == 'lstm':
cell_fn = rnn.BasicLSTMCell
else:
raise Exception("model type not supported: {}".format(args.model))
cells = []
for _ in range(args.num_layers):
cell = cell_fn(args.rnn_size)
cells.append(cell)
self.cell = cell = rnn.MultiRNNCell(cells)
self.input_data = tf.placeholder(tf.int32,
[args.batch_size, args.seq_length])
self.targets = tf.placeholder(tf.int32,
[args.batch_size, args.seq_length])
self.initial_state = cell.zero_state(args.batch_size, tf.float32)
self.batch_pointer = tf.Variable(0,
name="batch_pointer",
trainable=False,
dtype=tf.int32)
self.inc_batch_pointer_op = tf.assign(self.batch_pointer,
self.batch_pointer + 1)
self.epoch_pointer = tf.Variable(0,
name="epoch_pointer",
trainable=False)
self.batch_time = tf.Variable(0.0, name="batch_time", trainable=False)
tf.summary.scalar("time_batch", self.batch_time)
def variable_summaries(var):
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
#with tf.name_scope('stddev'):
# stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
#tf.summary.scalar('stddev', stddev)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
#tf.summary.histogram('histogram', var)
with tf.variable_scope('rnnlm'):
softmax_w = tf.get_variable("softmax_w",
[args.rnn_size, args.vocab_size])
variable_summaries(softmax_w)
softmax_b = tf.get_variable("softmax_b", [args.vocab_size])
variable_summaries(softmax_b)
with tf.device("/cpu:0"):
embedding = tf.get_variable("embedding",
[args.vocab_size, args.rnn_size])
inputs = tf.split(
tf.nn.embedding_lookup(embedding, self.input_data),
args.seq_length, 1)
inputs = [tf.squeeze(input_, [1]) for input_ in inputs]
def loop(prev, _):
prev = tf.matmul(prev, softmax_w) + softmax_b
prev_symbol = tf.stop_gradient(tf.argmax(prev, 1))
return tf.nn.embedding_lookup(embedding, prev_symbol)
outputs, last_state = legacy_seq2seq.rnn_decoder(
inputs,
self.initial_state,
cell,
loop_function=loop if infer else None,
scope='rnnlm')
output = tf.reshape(tf.concat(outputs, 1), [-1, args.rnn_size])
self.logits = tf.matmul(output, softmax_w) + softmax_b
self.probs = tf.nn.softmax(self.logits)
loss = legacy_seq2seq.sequence_loss_by_example(
[self.logits], [tf.reshape(self.targets, [-1])],
[tf.ones([args.batch_size * args.seq_length])], args.vocab_size)
self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length
tf.summary.scalar("cost", self.cost)
self.final_state = last_state
self.lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),
args.grad_clip)
optimizer = tf.train.AdamOptimizer(self.lr)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))
def getLayeredCell(layer_size, num_units, input_keep_prob,
output_keep_prob=1.0):
return rnn.MultiRNNCell([rnn.DropoutWrapper(rnn.BasicLSTMCell(num_units),
input_keep_prob, output_keep_prob) for i in range(layer_size)])
def main(_):
# load data, either shakespeare, or the Python source of Tensorflow itself
shakedir = FLAGS.text_dir
# shakedir = "../tensorflow/**/*.py"
codetext, valitext, bookranges = txt.read_data_files(shakedir,
validation=True)
# display some stats on the data
epoch_size = len(codetext) // (FLAGS.train_batch_size * FLAGS.seqlen)
txt.print_data_stats(len(codetext), len(valitext), epoch_size)
#
# the model (see FAQ in README.md)
#
lr = tf.placeholder(tf.float32, name='lr') # learning rate
pkeep = tf.placeholder(tf.float32, name='pkeep') # dropout parameter
batchsize = tf.placeholder(tf.int32, name='batchsize')
# inputs
X = tf.placeholder(tf.uint8, [None, None],
name='X') # [ BATCHSIZE, FLAGS.seqlen ]
Xo = tf.one_hot(X, ALPHASIZE, 1.0,
0.0) # [ BATCHSIZE, FLAGS.seqlen, ALPHASIZE ]
# expected outputs = same sequence shifted by 1 since we are trying to predict the next character
Y_ = tf.placeholder(tf.uint8, [None, None],
name='Y_') # [ BATCHSIZE, FLAGS.seqlen ]
Yo_ = tf.one_hot(Y_, ALPHASIZE, 1.0,
0.0) # [ BATCHSIZE, FLAGS.seqlen, ALPHASIZE ]
# input state
Hin = tf.placeholder(tf.float32, [None, INTERNALSIZE * NLAYERS],
name='Hin') # [ BATCHSIZE, INTERNALSIZE * NLAYERS]
# using a NLAYERS=3 layers of GRU cells, unrolled FLAGS.seqlen=30 times
# dynamic_rnn infers FLAGS.seqlen from the size of the inputs Xo
onecell = rnn.GRUCell(INTERNALSIZE)
dropcell = rnn.DropoutWrapper(onecell, input_keep_prob=pkeep)
multicell = rnn.MultiRNNCell([dropcell] * NLAYERS, state_is_tuple=False)
multicell = rnn.DropoutWrapper(multicell, output_keep_prob=pkeep)
Yr, H = tf.nn.dynamic_rnn(multicell,
Xo,
dtype=tf.float32,
initial_state=Hin)
# Yr: [ BATCHSIZE, FLAGS.seqlen, INTERNALSIZE ]
# H: [ BATCHSIZE, INTERNALSIZE*NLAYERS ] # this is the last state in the sequence
H = tf.identity(H, name='H') # just to give it a name
# Softmax layer implementation:
# Flatten the first two dimension of the output [ BATCHSIZE, FLAGS.seqlen, ALPHASIZE ] => [ BATCHSIZE x FLAGS.seqlen, ALPHASIZE ]
# then apply softmax readout layer. This way, the weights and biases are shared across unrolled time steps.
# From the readout point of view, a value coming from a cell or a minibatch is the same thing
Yflat = tf.reshape(
Yr, [-1, INTERNALSIZE]) # [ BATCHSIZE x FLAGS.seqlen, INTERNALSIZE ]
Ylogits = layers.linear(
Yflat, ALPHASIZE) # [ BATCHSIZE x FLAGS.seqlen, ALPHASIZE ]
Yflat_ = tf.reshape(
Yo_, [-1, ALPHASIZE]) # [ BATCHSIZE x FLAGS.seqlen, ALPHASIZE ]
loss = tf.nn.softmax_cross_entropy_with_logits(
logits=Ylogits, labels=Yflat_) # [ BATCHSIZE x FLAGS.seqlen ]
loss = tf.reshape(loss, [batchsize, -1]) # [ BATCHSIZE, FLAGS.seqlen ]
Yo = tf.nn.softmax(Ylogits,
name='Yo') # [ BATCHSIZE x FLAGS.seqlen, ALPHASIZE ]
Y = tf.argmax(Yo, 1) # [ BATCHSIZE x FLAGS.seqlen ]
Y = tf.reshape(Y, [batchsize, -1], name="Y") # [ BATCHSIZE, FLAGS.seqlen ]
train_step = tf.train.AdamOptimizer(lr).minimize(loss)
# stats for display
seqloss = tf.reduce_mean(loss, 1)
batchloss = tf.reduce_mean(seqloss)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(Y_, tf.cast(Y, tf.uint8)), tf.float32))
loss_summary = tf.summary.scalar("batch_loss", batchloss)
acc_summary = tf.summary.scalar("batch_accuracy", accuracy)
summaries = tf.summary.merge([loss_summary, acc_summary])
# Init Tensorboard stuff. This will save Tensorboard information into a different
# folder at each run named 'log/<timestamp>/'. Two sets of data are saved so that
# you can compare training and validation curves visually in Tensorboard.
timestamp = str(math.trunc(time.time()))
summary_writer = tf.summary.FileWriter(
os.path.join(FLAGS.summaries_dir, timestamp + "-training"))
validation_writer = tf.summary.FileWriter(
os.path.join(FLAGS.summaries_dir, timestamp + "-validation"))
# Init for saving models. They will be saved into a directory named 'checkpoints'.
# Only the last checkpoint is kept.
if not os.path.exists(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
saver = tf.train.Saver(max_to_keep=1)
# for display: init the progress bar
DISPLAY_FREQ = 50
_50_BATCHES = DISPLAY_FREQ * FLAGS.train_batch_size * FLAGS.seqlen
progress = txt.Progress(DISPLAY_FREQ,
size=111 + 2,
msg="Training on next " + str(DISPLAY_FREQ) +
" batches")
# init
istate = np.zeros([FLAGS.train_batch_size,
INTERNALSIZE * NLAYERS]) # initial zero input state
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
step = 0
# training loop
for x, y_, epoch in txt.rnn_minibatch_sequencer(codetext,
FLAGS.train_batch_size,
FLAGS.seqlen,
nb_epochs=1000):
# train on one minibatch
feed_dict = {
X: x,
Y_: y_,
Hin: istate,
lr: FLAGS.learning_rate,
pkeep: FLAGS.dropout_pkeep,
batchsize: FLAGS.train_batch_size
}
_, y, ostate, smm = sess.run([train_step, Y, H, summaries],
feed_dict=feed_dict)
# save training data for Tensorboard
summary_writer.add_summary(smm, step)
# display a visual validation of progress (every 50 batches)
if step % _50_BATCHES == 0:
feed_dict = {
X: x,
Y_: y_,
Hin: istate,
pkeep: 1.0,
batchsize: FLAGS.train_batch_size
} # no dropout for validation
y, l, bl, acc = sess.run([Y, seqloss, batchloss, accuracy],
feed_dict=feed_dict)
txt.print_learning_learned_comparison(x, y, l, bookranges, bl, acc,
epoch_size, step, epoch)
# run a validation step every 50 batches
# The validation text should be a single sequence but that's too slow (1s per 1024 chars!),
# so we cut it up and batch the pieces (slightly inaccurate)
# tested: validating with 5K sequences instead of 1K is only slightly more accurate, but a lot slower.
if step % _50_BATCHES == 0 and len(valitext) > 0:
VALI_SEQLEN = 1 * 1024 # Sequence length for validation. State will be wrong at the start of each sequence.
bsize = len(valitext) // VALI_SEQLEN
txt.print_validation_header(len(codetext), bookranges)
vali_x, vali_y, _ = next(
txt.rnn_minibatch_sequencer(valitext, bsize, VALI_SEQLEN,
1)) # all data in 1 batch
vali_nullstate = np.zeros([bsize, INTERNALSIZE * NLAYERS])
feed_dict = {
X: vali_x,
Y_: vali_y,
Hin: vali_nullstate,
pkeep: 1.0, # no dropout for validation
batchsize: bsize
}
ls, acc, smm = sess.run([batchloss, accuracy, summaries],
feed_dict=feed_dict)
txt.print_validation_stats(ls, acc)
# save validation data for Tensorboard
validation_writer.add_summary(smm, step)
# display a short text generated with the current weights and biases (every 150 batches)
if step // 3 % _50_BATCHES == 0:
txt.print_text_generation_header()
ry = np.array([[txt.convert_from_alphabet(ord("K"))]])
rh = np.zeros([1, INTERNALSIZE * NLAYERS])
for k in range(1000):
ryo, rh = sess.run([Yo, H],
feed_dict={
X: ry,
pkeep: 1.0,
Hin: rh,
batchsize: 1
})
rc = txt.sample_from_probabilities(
ryo, topn=10 if epoch <= 1 else 2)
print(chr(txt.convert_to_alphabet(rc)), end="")
ry = np.array([[rc]])
txt.print_text_generation_footer()
# save a checkpoint (every 500 batches)
if step // 10 % _50_BATCHES == 0:
saver.save(sess,
FLAGS.checkpoint_dir + '/rnn_train_' + timestamp,
global_step=step)
# display progress bar
progress.step(reset=step % _50_BATCHES == 0)
# loop state around
istate = ostate
step += FLAGS.train_batch_size * FLAGS.seqlen
def build_graph(self):
configs = self.trainingManager.configs
# shared between train and test
self.keep_prop_tf = tf.placeholder(dtype=tf.float32, name="keep_prop_tf")
# repeat it stacked_layers
encoder_dropcells_fw = [tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.GRUCell(configs.internal_state_encoder), output_keep_prob=self.keep_prop_tf) for _ in range(configs.stacked_layers)]
encoder_multi_cell_fw = rnn.MultiRNNCell(encoder_dropcells_fw, state_is_tuple=True)
encoder_dropcells_bw = [tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.GRUCell(configs.internal_state_encoder), output_keep_prob=self.keep_prop_tf) for _ in range(configs.stacked_layers)]
encoder_multi_cell_bw = rnn.MultiRNNCell(encoder_dropcells_bw, state_is_tuple=True)
decoder_dropcells = [tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.GRUCell(configs.internal_state_decoder), output_keep_prob=self.keep_prop_tf) for _ in range(configs.stacked_layers)]
decoder_multi_cell = rnn.MultiRNNCell(decoder_dropcells, state_is_tuple=True)
with tf.variable_scope('train'):
# input placeholders
self.encoder_inputs = tf.placeholder(shape=(None, None), dtype=tf.int32, name='encoder_inputs') # [en_seq_len, batch ]
self.encoder_inputs_length = tf.placeholder(shape=(None,), dtype=tf.int32, name='encoder_inputs_length') # [batch]
self.decoder_inputs = tf.placeholder(shape=(None, None), dtype=tf.int32, name='decoder_inputs') # [de_seq_len, batch] starts with KOKO_START token
self.decoder_inputs_length = tf.placeholder(shape=(None,), dtype=tf.int32, name='decoder_inputs_length') # [batch] IMPORTANT NOTE : decoder_inputs_length = counts the start token
self.decoder_outputs = tf.placeholder(shape=(None, None), dtype=tf.int32, name='decoder_outputs') # [de_seq_len, batch ] = ground truth padded at the end with zeros
# embedding lookup or one hot encoding of my languages (encoder / decoder)
if configs.use_embedding:
encoder_embeddings = tf.Variable(tf.random_uniform([configs.vocabulary_size_encoder, configs.encoder_embedding_size], -1.0, 1.0), dtype=tf.float32)
encoder_inputs_to_rnn = tf.nn.embedding_lookup(encoder_embeddings, self.encoder_inputs) # [ sequence_length,batch_size, encoder_embedding_size ] # embedded
decoder_embeddings = tf.Variable(tf.random_uniform([configs.vocabulary_size_decoder, configs.decoder_embedding_size], -1.0, 1.0), dtype=tf.float32)
decoder_inputs_to_rnn = tf.nn.embedding_lookup(decoder_embeddings, self.decoder_inputs) # [ sequence_length,batch_size, decoder_embedding_size ] # embedded
else:
encoder_inputs_to_rnn = tf.one_hot(self.encoder_inputs, configs.vocabulary_size_encoder, 1.0, 0.0) # [ sequence_length,batch_size, vocabulary_size ] # one hot encoded
decoder_inputs_to_rnn = tf.one_hot(self.decoder_inputs, configs.vocabulary_size_decoder, 1.0, 0.0) # [ sequence_length,batch_size, vocabulary_size ] # one hot encoded
(self.encoder_fw_outputs, self.encoder_bw_outputs), (encoder_fw_final_state, encoder_bw_final_state) = \
tf.nn.bidirectional_dynamic_rnn(cell_fw=encoder_multi_cell_fw,
cell_bw=encoder_multi_cell_bw,
inputs=encoder_inputs_to_rnn,
sequence_length=self.encoder_inputs_length,
dtype=tf.float32,
time_major=True)
# outputs :[sequence_length, batch_size, internal_state_encoder]
# final_state:[batch_size, internal_state_encoder] as tuple repeated stack times
# this is my thought vector = [batch_size, internal_state_encoder(fw)+internal_state_encoder(bw)]
# i will re-feed this thought vector at inference
self.decoder_init_state_from_encoder = tuple([tf.concat((encoder_fw_final_state[i], encoder_bw_final_state[i]), axis=1) for i in range(configs.stacked_layers)]) # state is tuple for decoder input
# decoder dynamic rnn
self.decoder_states_outputs, self.decoder_final_state = tf.nn.dynamic_rnn(decoder_multi_cell,
inputs=decoder_inputs_to_rnn,
initial_state=self.decoder_init_state_from_encoder,
time_major=True,
sequence_length=self.decoder_inputs_length)
# decoder_states_outputs :[sequence_length, batch_size, internal_state_decoder]
# decoder_final_state :[batch_size, internal_state_decoder] as tuple repeated stack times
decoder_logits = tf.layers.dense(self.decoder_states_outputs, units=configs.vocabulary_size_decoder, use_bias=True) # projection on the vocabulary outputs : [sequence_length, batch_size, vocabulary_size_decoder]
self.dec_probabilities = tf.nn.softmax(decoder_logits) # [sequence_length, batch_size, vocabulary_size_decoder]
# the triangle has the decoder shape not the encoder !!!!
lower_triangular_ones = tf.constant(np.tril(np.ones([configs.max_seq_len_decoder, configs.max_seq_len_decoder])), dtype=tf.float32) # lower triangle ones [max_seq_len_encoder,max_seq_len_encoder] >> [[1. 0.],[1. 1.]]
_, batch_size_tf = tf.unstack(tf.shape(self.encoder_inputs)) # seq_length , batch_size
seqlen_mask = tf.transpose(tf.slice(tf.gather(lower_triangular_ones, self.decoder_inputs_length - 1), begin=[0, 0], size=[batch_size_tf, tf.reduce_max(self.decoder_inputs_length)])) # so you need to take length -1 due to lower triangle ones [sequence_length, batch_size]
# connect outputs to
with tf.name_scope("optimization"):
# Loss function
self.loss = tf.contrib.seq2seq.sequence_loss(decoder_logits, self.decoder_outputs, seqlen_mask) # sparse softmax cross entropy
# Optimizer
self.train_step = tf.train.RMSPropOptimizer(configs.learning_rate).minimize(self.loss)
# To calculate the number correct, this means we don't count the padded as correct
correct = tf.cast(tf.equal(tf.cast(tf.argmax(decoder_logits, 2), tf.int32), self.decoder_outputs), dtype=tf.float32) * seqlen_mask
self.accuracy = tf.reduce_sum(correct) / tf.reduce_sum(seqlen_mask)
# summary tensors
if not self.trainingManager.is_local_env:
loss_summary = tf.summary.scalar("batch_loss", self.loss)
acc_summary = tf.summary.scalar("batch_accuracy", self.accuracy)
self.summaries = tf.summary.merge([loss_summary, acc_summary])
o = [0, 0, 0, 1]
x_data = np.array([[h, e, l, l, o],
[e, o, l, l, l],
[l, l, e, e, l]], dtype=np.float32)
# with tf.variable_scope('initial_state') as scope:
# batch_size = 3
# pp.pprint(x_data)
#
# # One cell RNN input_dim (4) -> output_dim (5). sequence: 5, batch: 3
# hidden_size = 2
# cell = rnn.BasicLSTMCell(num_units=hidden_size, state_is_tuple=True)
# initial_state = cell.zero_state(batch_size, tf.float32)
# outputs, _states = tf.nn.dynamic_rnn(cell, x_data,
# initial_state=initial_state, dtype=tf.float32)
# sess.run(tf.global_variables_initializer())
# pp.pprint(outputs.eval())
with tf.variable_scope('MultiRNNCell') as scope:
# Make rnn
# cell = rnn.BasicLSTMCell(num_units=5, state_is_tuple=True)
def lstm_cell():
cell = rnn.BasicLSTMCell(5, state_is_tuple=True)
return cell
cells = rnn.MultiRNNCell([lstm_cell() for _ in range(3)], state_is_tuple=True) # 3 layers
# print(x_data)
# rnn in/out
outputs, _states = tf.nn.dynamic_rnn(cells, x_data, dtype=tf.float32)
print("dynamic rnn: ", outputs)
sess.run(tf.global_variables_initializer())
pp.pprint(outputs.eval()) # batch size, unrolling (time), hidden_size
def __init__(self, params):
"""
:param params:是一个字典，包含num_steps,state_size,batch_size,num_classes,learning_rate
"""
self.params = params
n_steps = params["n_steps"]
n_input = params["n_input"]
n_units = params["n_units"]
n_classes = params["n_classes"]
batch_size = params["batch_size"]
# "n_steps": 128,
# "n_input": 128,
# "n_units": 128,
# "n_classes": 6,
# "batch_size": 100,
# "n_epochs": 50,
# "learning_rate": 0.0003,
# "display_step": 1,
# "run_mode": "/cpu:0",
# "split_png_data": "/Users/jw/Desktop/audio_data/1484131952_256_0.5/split_png_data/CASIA"
tf.reset_default_graph()
with tf.get_default_graph().as_default():
with tf.name_scope("placeholder"):
self.x = tf.placeholder("float", [None, n_steps * n_input],
name="x")
self.input = tf.reshape(self.x, [-1, n_steps, n_input])
self.y = tf.placeholder("float", [None, n_classes], name="y")
self.keep_prob = tf.placeholder(tf.float32)
with tf.variable_scope("softmax"):
weights = tf.Variable(tf.random_normal([n_units, n_classes]),
name='weights')
biases = tf.Variable(tf.random_normal([n_classes]),
name='biases')
# x = tf.transpose(self.x, [1, 0, 2])
# x = tf.reshape(x, [-1, n_input])
# x = tf.split(0, n_steps, x)
sequence_length = np.zeros([batch_size], dtype=int)
sequence_length += n_steps
state_size = self.params["n_units"]
num_layers = self.params["n_layers"]
cell_type = self.params["cell_type"]
num_weights_for_custom_cell = self.params.get("n_weights")
if cell_type == 'Custom':
cell = CustomCell(state_size, num_weights_for_custom_cell)
cell = rnn.MultiRNNCell([
rnn.DropoutWrapper(rnn.LSTMCell(state_size,
state_is_tuple=True),
input_keep_prob=self.keep_prob)
for _ in range(num_layers)
])
elif cell_type == 'GRU':
cell = rnn.GRUCell(state_size)
elif cell_type == 'LSTM':
cell = rnn.MultiRNNCell([
rnn.DropoutWrapper(rnn.LSTMCell(state_size,
state_is_tuple=True),
input_keep_prob=self.keep_prob)
for _ in range(num_layers)
])
elif cell_type == 'LN_LSTM':
cell = LayerNormalizedLSTMCell(state_size)
else:
cell = rnn.BasicRNNCell(state_size)
cell = rnn.DropoutWrapper(cell, output_keep_prob=self.keep_prob)
self.init_state = cell.zero_state(batch_size, dtype=tf.float32)
outputs, self.final_state = tf.nn.dynamic_rnn(
cell,
self.input,
dtype=tf.float32,
initial_state=self.init_state,
sequence_length=sequence_length)
# outputs's shape [batch_size, time_step, state_size]
outputs = tf.transpose(outputs, [1, 0, 2])
pred = tf.matmul(outputs[-1], weights) + biases
self.cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=pred,
labels=self.y))
self.optimizer = tf.train.AdamOptimizer(learning_rate=params['learning_rate']) \
.minimize(self.cost)
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(self.y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
tf.summary.scalar("cost", self.cost)
tf.summary.scalar("accuracy", self.accuracy)
self.merge_summary_op = tf.summary.merge_all()
logger.info("模型构建完毕")
X = tf.reshape(_X, [-1, 28, 28])
y = tf.placeholder(tf.float32, [None, class_num])
keep_prob = tf.placeholder(tf.float32)
def unit_lstm():
lstm_cell = rnn.BasicLSTMCell(num_units=hidden_size,
forget_bias=1.0,
state_is_tuple=True)
lstm_cell = rnn.DropoutWrapper(cell=lstm_cell,
input_keep_prob=1.0,
output_keep_prob=keep_prob)
return lstm_cell
mlstm_cell = rnn.MultiRNNCell([unit_lstm() for i in range(3)],
state_is_tuple=True)
init_state = mlstm_cell.zero_state(batch_size, dtype=tf.float32)
outputs, state = tf.nn.dynamic_rnn(mlstm_cell,
inputs=X,
initial_state=init_state,
time_major=False)
h_state = outputs[:, -1, :]
W = tf.Variable(tf.truncated_normal([hidden_size, class_num], stddev=0.1),
dtype=tf.float32)
bias = tf.Variable(tf.constant(0.1, shape=[class_num]), dtype=tf.float32)
y_pre = tf.nn.softmax(tf.matmul(h_state, W) + bias)
cross_entropy = -tf.reduce_mean(y * tf.log(y_pre))
train_op = tf.train.AdamOptimizer(lr).minimize(cross_entropy)
time_step = 1
input_num = 8
output_num = 1
epoch_num = 50
batch_size = 72
learning_rate = 0.001
with tf.device('/gpu:0'):
x = tf.placeholder("float", [None, time_step, input_num])
y = tf.placeholder("float", [None, output_num])
def lstm_cell():
lstm_cell = rnn.BasicLSTMCell(hidden_num)
return lstm_cell
with tf.variable_scope("lstm", reuse=None):
Multi_cell = rnn.MultiRNNCell([lstm_cell() for _ in range(layer_num)],
state_is_tuple=True)
outputs, _ = tf.nn.dynamic_rnn(Multi_cell, x, dtype=tf.float32)
prediction = tf.layers.dense(inputs=outputs[:, -1, :],
units=output_num)
loss = tf.reduce_mean(tf.abs(prediction - y))
train_step = tf.train.AdamOptimizer().minimize(loss)
init = tf.global_variables_initializer()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True)) as sess:
sess.run(init)
loss_epoch = [0] * epoch_num
xx = 0
n = 0
pre = []
rmse = 0
start_train = time.time()
#'cnnoutscale': tf.Variable(tf.ones([2048])),
#'featbeta': tf.Variable(tf.zeros([4096])),
#'featscale': tf.Variable(tf.ones([4096])),
#'gbeta': tf.Variable(tf.zeros([1000])),
#'gscale': tf.Variable(tf.ones([1000]))
}
# question-embedding
#embed_ques_W = tf.Variable(tf.random_uniform([vocabulary_size, input_embedding_size], -0.08, 0.08), name='embed_ques_W')
# encoder: RNN body
lstm_1 = rnn_cell.LSTMCell(rnn_size, input_embedding_size, use_peepholes=True, state_is_tuple=False)
lstm_dropout_1 = rnn_cell.DropoutWrapper(lstm_1, output_keep_prob = 1 - dropout_rate)
lstm_2 = rnn_cell.LSTMCell(rnn_size, rnn_size, use_peepholes=True, state_is_tuple=False)
lstm_dropout_2 = rnn_cell.DropoutWrapper(lstm_2, output_keep_prob = 1 - dropout_rate)
stacked_lstm = rnn_cell.MultiRNNCell([lstm_dropout_1, lstm_dropout_2], state_is_tuple=False)
image = tf.placeholder(tf.float32, [batch_size, 2048])
question = tf.placeholder(tf.int32, [batch_size, max_words_q])
#answers_true = tf.placeholder(tf.float32, (batch_size, 1000))
#noise = tf.placeholder(tf.float32, [batch_size, 4096])
#answers_false = tf.placeholder(tf.float32, (None, 1000))
#image_false = tf.placeholder(tf.float32, (None, 2048))
#question_false = tf.placeholder(tf.int32, [batch_size, max_words_q])
#state = tf.zeros([batch_size, stacked_lstm.state_size])
state = stacked_lstm.zero_state(batch_size, tf.float32)
loss = 0.0
def __init__(self,
num_emb,
batch_size,
emb_dim,
hidden_dim,
sequence_length,
start_token,
learning_rate=0.01,
reward_gamma=0.95):
self.num_emb = num_emb
self.batch_size = batch_size
self.emb_dim = emb_dim
self.hidden_dim = hidden_dim
self.sequence_length = sequence_length
self.start_token = tf.constant([start_token] * self.batch_size,
dtype=tf.int32)
self.learning_rate = tf.Variable(float(learning_rate), trainable=False)
self.reward_gamma = reward_gamma
self.temperature = 1.0
self.grad_clip = 5.0
self.expected_reward = tf.Variable(tf.zeros([self.sequence_length]))
with tf.variable_scope('generator') as scope:
self.g_embeddings = tf.Variable(
self.init_matrix([self.num_emb, self.emb_dim]))
self.g_recurrent_unit = self.create_recurrent_unit(
) # maps h_tm1 to h_t for generator
self.g_output_unit = self.create_output_unit(
) # maps h_t to o_t (output token logits)
# placeholder definition
self.x = tf.placeholder(
tf.int32, shape=[self.batch_size, self.sequence_length])
# sequence of indices of true data, not including start token
self.rewards = tf.placeholder(
tf.float32, shape=[self.batch_size, self.sequence_length])
# get from rollout policy and discriminator
# processed for batch
with tf.device("/cpu:0"):
inputs = tf.split(axis=1,
num_or_size_splits=self.sequence_length,
value=tf.nn.embedding_lookup(
self.g_embeddings, self.x))
self.processed_x = tf.stack([
tf.squeeze(input_, [1]) for input_ in inputs
]) # seq_length x batch_size x emb_dim
cell = rnn.BasicLSTMCell(self.hidden_dim, state_is_tuple=True)
self.cell = rnn.MultiRNNCell([cell] * 2, state_is_tuple=True)
self.h0 = tf.zeros([self.batch_size, self.hidden_dim])
self.h0 = tf.stack([self.h0, self.h0])
self.h0 = self.cell.zero_state(self.batch_size, tf.float32)
gen_o = tensor_array_ops.TensorArray(dtype=tf.float32,
size=self.sequence_length,
dynamic_size=False,
infer_shape=True)
gen_x = tensor_array_ops.TensorArray(dtype=tf.int32,
size=self.sequence_length,
dynamic_size=False,
infer_shape=True)
def _g_recurrence(x_t, h_tm1, gen_o, gen_x):
h_t = self.g_recurrent_unit(x_t, h_tm1) # hidden_memory_tuple
o_t = self.g_output_unit(
h_t) # batch x vocab , logits not prob
log_prob = tf.log(tf.nn.softmax(o_t))
next_token = tf.cast(
tf.reshape(tf.multinomial(log_prob, 1), [self.batch_size]),
tf.int32)
x_tp1 = tf.nn.embedding_lookup(self.g_embeddings,
next_token) # batch x emb_dim
gen_o = gen_o.write(i,
tf.reduce_sum(
tf.multiply(
tf.one_hot(next_token,
self.num_emb, 1.0, 0.0),
tf.nn.softmax(o_t)),
1)) # [batch_size] , prob
gen_x = gen_x.write(i, next_token) # indices, batch_size
return x_tp1, h_t, gen_o, gen_x
# My loop
initial_state = (tf.zeros([self.batch_size,
self.hidden_dim]), self.h0)
x_t, h_t, gen_o, gen_x = tf.nn.embedding_lookup(
self.g_embeddings,
self.start_token), initial_state, gen_o, gen_x
for i in range(self.sequence_length):
if i > 0: scope.reuse_variables()
x_t, h_t, gen_o, gen_x = _g_recurrence(x_t, h_t, gen_o, gen_x)
self.gen_o, self.gen_x = gen_o, gen_x
self.gen_x = self.gen_x.stack() # seq_length x batch_size
self.gen_x = tf.transpose(self.gen_x,
perm=[1, 0]) # batch_size x seq_length
# supervised pretraining for generator
g_predictions = tensor_array_ops.TensorArray(
dtype=tf.float32,
size=self.sequence_length,
dynamic_size=False,
infer_shape=True)
g_logits = tensor_array_ops.TensorArray(dtype=tf.float32,
size=self.sequence_length,
dynamic_size=False,
infer_shape=True)
ta_emb_x = tensor_array_ops.TensorArray(dtype=tf.float32,
size=self.sequence_length)
ta_emb_x = ta_emb_x.unstack(self.processed_x)
def _pretrain_recurrence(x_t, h_tm1, g_predictions, g_logits):
h_t = self.g_recurrent_unit(x_t, h_tm1)
o_t = self.g_output_unit(h_t)
g_predictions = g_predictions.write(
i, tf.nn.softmax(o_t)) # batch x vocab_size
g_logits = g_logits.write(i, o_t) # batch x vocab_size
x_tp1 = ta_emb_x.read(i)
return x_tp1, h_t, g_predictions, g_logits
initial_state = (tf.zeros([self.batch_size,
self.hidden_dim]), self.h0)
x_t, ht = tf.nn.embedding_lookup(self.g_embeddings,
self.start_token), initial_state
for i in range(self.sequence_length):
if i > 0: scope.reuse_variables()
x_t, h_t, g_predictions, g_logits = _pretrain_recurrence(
x_t, h_t, g_predictions, g_logits)
self.g_predictions, self.g_logits = g_predictions, g_logits
self.g_predictions = tf.transpose(
self.g_predictions.stack(),
perm=[1, 0, 2]) # batch_size x seq_length x vocab_size
self.g_logits = tf.transpose(
self.g_logits.stack(),
perm=[1, 0, 2]) # batch_size x seq_length x vocab_size
# pretraining loss
self.pretrain_loss = -tf.reduce_sum(
tf.one_hot(tf.to_int32(tf.reshape(
self.x, [-1])), self.num_emb, 1.0, 0.0) * tf.log(
tf.clip_by_value(
tf.reshape(self.g_predictions, [-1, self.num_emb]),
1e-20, 1.0))) / (self.sequence_length *
self.batch_size)
# training updates
pretrain_opt = self.g_optimizer(self.learning_rate)
tvars = tf.trainable_variables()
g_params = [var for var in tvars if 'generator' in var.name]
self.pretrain_grad, _ = tf.clip_by_global_norm(
tf.gradients(self.pretrain_loss, g_params), self.grad_clip)
self.pretrain_updates = pretrain_opt.apply_gradients(
zip(self.pretrain_grad, g_params))
#######################################################################################################
# Unsupervised Training
#######################################################################################################
self.g_loss = -tf.reduce_sum(
tf.reduce_sum(
tf.one_hot(tf.to_int32(tf.reshape(
self.x, [-1])), self.num_emb, 1.0, 0.0) * tf.log(
tf.clip_by_value(
tf.reshape(self.g_predictions, [-1, self.num_emb]),
1e-20, 1.0)), 1) * tf.reshape(self.rewards, [-1]))
g_opt = self.g_optimizer(self.learning_rate)
self.g_grad, _ = tf.clip_by_global_norm(
tf.gradients(self.g_loss, g_params), self.grad_clip)
self.g_updates = g_opt.apply_gradients(zip(self.g_grad, g_params))
def __init__(self,
encoder_inputs,
encoder_lengths,
encoder_inputs_2,
encoder_lengths_2,
decoder_inputs,
decoder_lengths,
_embed_ph,
learn_rate,
start_token,
if_test=False,
temperature=None,
end_rate=1):
self.start_tokens = tf.constant([start_token] * BATCH_SIZE,
dtype=tf.int32)
self.learn_rate = learn_rate
keep_prob = 0.8
with tf.variable_scope('generator',
initializer=tf.orthogonal_initializer()):
with tf.variable_scope('embedding'):
self.embedding = tf.get_variable(
name='embedding',
shape=[param.VOCAB_SIZE, INPUT_DIM],
trainable=True)
self._embed_ph = _embed_ph
self._embed_init = self.embedding.assign(self._embed_ph)
self.encoder_inputs = encoder_inputs
self.encoder_lengths = encoder_lengths
if param.Use_VAE:
self.encoder_inputs_2 = encoder_inputs_2
self.encoder_lengths_2 = encoder_lengths_2
self.decoder_lengths = decoder_lengths
self.decoder_inputs = decoder_inputs[:, :-1]
max_len = tf.shape(decoder_inputs)[-1]
with tf.variable_scope('cell', initializer=xavier_initializer()):
self.encoder_cell = rnn.MultiRNNCell([
rnn.BasicLSTMCell(_NUM_UNITS, activation=tf.tanh)
for _ in range(param.NUM_LAYERS)
])
self.encoder_init_state = self.encoder_cell.zero_state(
BATCH_SIZE, dtype=tf.float32)
_, self.encoder_final_state = tf.nn.dynamic_rnn(
cell=self.encoder_cell,
initial_state=self.encoder_init_state,
inputs=self.encoder_inputs,
sequence_length=self.encoder_lengths,
scope='encoder')
if param.Use_VAE:
self.encoder_cell_2 = rnn.MultiRNNCell([
rnn.BasicLSTMCell(_NUM_UNITS, activation=tf.tanh)
for _ in range(param.NUM_LAYERS)
])
self.encoder_init_state_2 = self.encoder_cell.zero_state(
BATCH_SIZE, dtype=tf.float32)
_, self.encoder_final_state_2 = tf.nn.dynamic_rnn(
cell=self.encoder_cell_2,
initial_state=self.encoder_init_state_2,
inputs=tf.nn.embedding_lookup(self.embedding,
self.decoder_inputs),
sequence_length=self.decoder_lengths,
scope='encoder_2')
self.decoder_cell = rnn.MultiRNNCell([
rnn.BasicLSTMCell(_NUM_UNITS)
for _ in range(param.NUM_LAYERS)
])
self.decoder_cell_drop = tf.contrib.rnn.DropoutWrapper(
self.decoder_cell, output_keep_prob=keep_prob)
self.decoder_init_state = self.decoder_cell_drop.zero_state(
BATCH_SIZE, dtype=tf.float32)
if param.Use_VAE:
self.decoder_input_state_2 = self.encoder_final_state_2
# self.decoder_input_state = (LSTMStateTuple(self.decoder_input_state[0,0],self.decoder_input_state[0,1]),
# LSTMStateTuple(self.decoder_input_state[1,0],self.decoder_input_state[1,1]))
self.decoder_input_state = self.encoder_final_state
if Use_relational_memory:
g_output_unit = create_output_unit(
_NUM_UNITS * NUM_LAYERS * 2, param.VOCAB_SIZE)
mem_slots = param.mem_slots
head_size = param.head_size
num_heads = param.num_heads
gen_mem = RelationalMemory(mem_slots=mem_slots,
head_size=head_size,
num_heads=num_heads)
self.decoder_inputs = tf.pad(self.decoder_inputs,
[[0, 0], [0, 1]])
x_emb = tf.transpose(
tf.nn.embedding_lookup(self.embedding,
self.decoder_inputs),
perm=[1, 0, 2]) # seq_len x batch_size x emb_dim
g_predictions = tensor_array_ops.TensorArray(
dtype=tf.float32,
size=max_len,
dynamic_size=True,
infer_shape=True)
g_predictions = g_predictions.write(
0,
tf.one_hot(self.decoder_inputs[:, 0],
param.VOCAB_SIZE))
ta_emb_x = tensor_array_ops.TensorArray(dtype=tf.float32,
size=max_len)
ta_emb_x = ta_emb_x.unstack(x_emb)
with tf.variable_scope('postprocessing',
initializer=xavier_initializer()):
if Post_with_state:
self.softmax_w = tf.get_variable(
'softmax_w', [
head_size * num_heads + 2 * NUM_UNITS,
param.VOCAB_SIZE
])
else:
self.softmax_w = tf.get_variable(
'softmax_w',
[head_size * num_heads, param.VOCAB_SIZE])
self.softmax_b = tf.get_variable(
'softmax_b', [param.VOCAB_SIZE])
# the generator recurrent moddule used for pre-training
def _pretrain_recurrence(i, x_t, h_tm1, g_predictions):
mem_o_t, h_t = gen_mem(x_t, h_tm1)
if Post_with_state:
mem_o_t = tf.concat([
tf.reshape(mem_o_t,
[-1, head_size * num_heads]),
self.decoder_input_state[:, 0, :2 * NUM_UNITS]
], -1)
else:
mem_o_t = tf.reshape(mem_o_t,
[-1, head_size * num_heads])
o_t = tf.nn.bias_add(tf.matmul(mem_o_t,
self.softmax_w),
bias=self.softmax_b)
#o_t = g_output_unit(mem_o_t)
g_predictions = g_predictions.write(
i, o_t) # batch_size x vocab_size
x_tp1 = ta_emb_x.read(i)
return i + 1, x_tp1, h_t, g_predictions
self.decoder_input_state = tf.convert_to_tensor(
self.decoder_input_state)
self.decoder_input_state = tf.transpose(
self.decoder_input_state, perm=[2, 0, 1, 3])
self.decoder_input_state = tf.reshape(
self.decoder_input_state, [BATCH_SIZE, mem_slots, -1])
if param.Use_latent_z:
self.decoder_input_state = tf.concat([
tf.truncated_normal(
shape=self.decoder_input_state.shape),
self.decoder_input_state
],
axis=-1)
elif Use_VAE:
self.decoder_input_state_2 = tf.convert_to_tensor(
self.decoder_input_state_2)
self.decoder_input_state_2 = tf.transpose(
self.decoder_input_state_2, perm=[2, 0, 1, 3])
self.decoder_input_state_2 = tf.reshape(
self.decoder_input_state_2, [BATCH_SIZE, -1])
self.mn = tf.layers.dense(self.decoder_input_state_2,
units=NUM_UNITS * 2)
self.sd = 0.5 * tf.layers.dense(
self.decoder_input_state_2, units=NUM_UNITS * 2)
epsilon = tf.random_normal(
tf.stack([
tf.shape(self.decoder_input_state_2)[0],
NUM_UNITS * 2
]))
self.decoder_input_state_2 = self.mn + tf.multiply(
epsilon, tf.exp(self.sd))
self.decoder_input_state_2 = tf.reshape(
self.decoder_input_state_2,
[BATCH_SIZE, mem_slots, -1])
self.decoder_input_state = tf.concat([
self.decoder_input_state_2,
self.decoder_input_state
],
axis=-1)
# build a graph for outputting sequential tokens
_, _, self.decoder_final_state, self.outputs = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3: i < max_len,
body=_pretrain_recurrence,
loop_vars=(tf.constant(1, dtype=tf.int32),
tf.nn.embedding_lookup(
self.embedding, self.decoder_inputs[:,
0]),
self.decoder_input_state, g_predictions))
self.logits = tf.transpose(self.outputs.stack()[1:, :, :],
perm=[1, 0, 2])
else:
self.outputs, self.decoder_final_state = tf.nn.dynamic_rnn(
cell=self.decoder_cell_drop,
initial_state=self.decoder_input_state, #初始状态,h
inputs=tf.nn.embedding_lookup(
self.embedding, self.decoder_inputs), #输入x
sequence_length=self.decoder_lengths,
dtype=tf.float32,
scope='decoder')
#这里直接用softmax
if Use_relational_memory:
pass
else:
# self.logits = g_output_unit(tf.reshape(self.outputs, [-1, _NUM_UNITS]))
with tf.variable_scope('cell/postprocessing',
initializer=xavier_initializer()):
self.softmax_w = tf.get_variable(
'softmax_w', [_NUM_UNITS, param.VOCAB_SIZE])
self.softmax_b = tf.get_variable('softmax_b',
[param.VOCAB_SIZE])
self.logits = tf.nn.bias_add(tf.matmul(
tf.reshape(self.outputs, [-1, _NUM_UNITS]),
self.softmax_w),
bias=self.softmax_b)
self.probs = tf.reshape(
tf.nn.softmax(self.logits),
[BATCH_SIZE, -1, param.VOCAB_SIZE]) #输出应该是一个one_shot向量
self.labels = tf.one_hot(decoder_inputs[:, 1:],
depth=param.VOCAB_SIZE,
dtype=tf.int32)
self.right_count = tf.reduce_sum(
tf.reduce_sum(
tf.multiply(
tf.one_hot(tf.argmax(self.probs,
-1), param.VOCAB_SIZE),
tf.to_float(self.labels)), -1),
-1) / tf.to_float(max_len)
self.logits = tf.reshape(self.logits,
[BATCH_SIZE, -1, param.VOCAB_SIZE])
loss = get_loss(LOSS_TYPE, self.logits, self.labels,
self.decoder_lengths, BATCH_SIZE)
self.loss = loss
self.original_loss = get_loss(1, self.logits, self.labels,
self.decoder_lengths, BATCH_SIZE)
# ---------- generate tokens and approximated one-hot results (Adversarial) ---------
gen_o = tensor_array_ops.TensorArray(dtype=tf.float32,
size=0,
dynamic_size=True,
infer_shape=True) #the prob
gen_x = tensor_array_ops.TensorArray(
dtype=tf.int32, size=0, dynamic_size=True,
infer_shape=True) # sampled token
gen_x_onehot_adv = tensor_array_ops.TensorArray(
dtype=tf.float32, size=0, dynamic_size=True,
infer_shape=True) # generator output (relaxed of gen_x)
random_start_length = tf.constant(param.start_length,
dtype=tf.int32)
# random_start_length = tf.random_uniform(shape = [],minval=0,maxval=sentence_min_len,dtype=tf.int32)
def _start_recurrence(word_i, gen_o, gen_x, gen_x_onehot_adv):
gen_x = gen_x.write(word_i, decoder_inputs[:, word_i])
gen_o = gen_o.write(
word_i,
tf.one_hot(decoder_inputs[:, word_i], param.VOCAB_SIZE,
1.0, 0.0))
gen_x_onehot_adv = gen_x_onehot_adv.write(
word_i,
tf.one_hot(decoder_inputs[:, word_i], param.VOCAB_SIZE,
1.0, 0.0) * 1000000)
return word_i + 1, gen_o, gen_x, gen_x_onehot_adv
_, gen_o, gen_x, gen_x_onehot_adv = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3: i < random_start_length + 1,
body=_start_recurrence,
loop_vars=(tf.constant(0), gen_o, gen_x, gen_x_onehot_adv))
#temperature = param.temperature
# the generator recurrent module used for adversarial training
if Use_relational_memory:
with tf.variable_scope('cell/postprocessing', reuse=True):
if Post_with_state:
self.softmax_w = tf.get_variable(
'softmax_w', [
param.head_size * param.num_heads +
2 * NUM_UNITS, param.VOCAB_SIZE
])
else:
self.softmax_w = tf.get_variable(
'softmax_w', [
param.head_size * param.num_heads,
param.VOCAB_SIZE
])
self.softmax_b = tf.get_variable('softmax_b',
[param.VOCAB_SIZE])
if param.start_length >= 1:
x_emb = tf.transpose(tf.nn.embedding_lookup(
self.embedding, self.decoder_inputs),
perm=[1, 0, 2])
ta_emb_gen_x = tensor_array_ops.TensorArray(
dtype=tf.float32, size=max_len)
ta_emb_gen_x = ta_emb_gen_x.unstack(x_emb)
# the generator recurrent moddule used for pre-training
def _start_rel_recurrence(i, x_t, h_tm1):
mem_o_t, h_t = gen_mem(x_t, h_tm1)
if Post_with_state:
mem_o_t = tf.concat([
tf.reshape(mem_o_t,
[-1, head_size * num_heads]),
self.decoder_input_state[:, 0, :2 * NUM_UNITS]
], -1)
else:
mem_o_t = tf.reshape(mem_o_t,
[-1, head_size * num_heads])
o_t = tf.nn.bias_add(tf.matmul(mem_o_t,
self.softmax_w),
bias=self.softmax_b)
x_tp1 = ta_emb_gen_x.read(i)
return i + 1, x_tp1, h_t
self.decoder_start_word_state = \
tf.cond(random_start_length > 0,
lambda: (control_flow_ops.while_loop(
cond=lambda i, _1, _2: i < random_start_length + 1,
body=_start_rel_recurrence,
loop_vars=(
tf.constant(1, dtype=tf.int32),
tf.nn.embedding_lookup(self.embedding, self.decoder_inputs[:,0]),
self.decoder_input_state)))[2],
lambda: self.decoder_input_state)
# _, _, self.decoder_start_word_state = control_flow_ops.while_loop(
# cond=lambda i, _1, _2: i < random_start_length + 1,
# body=_start_rel_recurrence,
# loop_vars=(
# tf.constant(1, dtype=tf.int32),
# tf.nn.embedding_lookup(self.embedding, self.decoder_inputs[:,0]),
# self.decoder_input_state))
else:
self.decoder_start_word_state = self.decoder_input_state
else:
with tf.variable_scope('cell', reuse=True):
_, self.decoder_start_word_state = tf.nn.dynamic_rnn(
cell=self.decoder_cell_drop,
initial_state=self.encoder_final_state, # 初始状态,h
inputs=tf.nn.embedding_lookup(
self.embedding, self.decoder_inputs), # 输入x
sequence_length=np.ones(BATCH_SIZE) *
random_start_length,
dtype=tf.float32,
scope='decoder')
def _gen_recurrence(i, x_t, state, gen_o, gen_x, gen_x_onehot_adv):
if Use_relational_memory:
mem_o_t, state = gen_mem(x_t, state) # hidden_memory_tuple
if Post_with_state:
mem_o_t = tf.concat([
tf.reshape(mem_o_t, [-1, head_size * num_heads]),
self.decoder_input_state[:, 0, :2 * NUM_UNITS]
], -1)
else:
mem_o_t = tf.reshape(mem_o_t,
[-1, head_size * num_heads])
logits = tf.nn.bias_add(tf.matmul(mem_o_t, self.softmax_w),
bias=self.softmax_b)
pad = np.ones((BATCH_SIZE, VOCAB_SIZE))
pad_2 = tf.one_hot(tf.to_int32(
tf.constant(np.ones((BATCH_SIZE)) * 2)),
depth=VOCAB_SIZE)
pad_2 = tf.multiply(pad_2, end_rate - 1)
pad = tf.constant(pad)
pad = tf.add(tf.to_float(pad), tf.to_float(pad_2))
logits = tf.multiply(logits, pad)
else:
with tf.variable_scope('cell', reuse=True):
outputs, state = rnn.static_rnn(
cell=self.decoder_cell_drop,
initial_state=state,
inputs=[x_t], #输入x
sequence_length=np.ones(BATCH_SIZE),
dtype=tf.float32,
scope='decoder')
with tf.variable_scope('postprocessing', reuse=True):
logits = tf.nn.bias_add(tf.matmul(
tf.reshape(outputs, [-1, _NUM_UNITS]),
self.softmax_w),
bias=self.softmax_b)
# logits = g_output_unit(tf.reshape(outputs, [-1, _NUM_UNITS]))
prob = tf.reshape(
tf.nn.softmax(logits),
[BATCH_SIZE, param.VOCAB_SIZE]) #without length
if not if_test:
gumbel_t = add_gumbel(logits)
else:
# gumbel_t = logits
gumbel_t = add_gumbel(tf.multiply(1.2, logits))
next_token = tf.to_int32(
tf.stop_gradient(tf.argmax(gumbel_t, axis=1)))
next_token_onehot = tf.one_hot(next_token, param.VOCAB_SIZE,
1.0, 0.0)
x_onehot_appr = tf.multiply(
gumbel_t,
temperature) # one-hot-like, [batch_size x vocab_size]
gen_o = gen_o.write(i, logits)
gen_x = gen_x.write(i, next_token)
gen_x_onehot_adv = gen_x_onehot_adv.write(
i, tf.nn.softmax(x_onehot_appr))
x_tp1 = tf.nn.embedding_lookup(self.embedding, next_token)
return i + 1, x_tp1, state, gen_o, gen_x, gen_x_onehot_adv
# build a graph for outputting sequential tokens
_, _, _, self.gen_o, self.gen_x, self.gen_x_onehot_adv = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3, _4, _5: i < max_len,
body=_gen_recurrence,
loop_vars=(random_start_length + 1,
tf.nn.embedding_lookup(
self.embedding,
decoder_inputs[:, random_start_length]),
self.decoder_start_word_state, gen_o, gen_x,
gen_x_onehot_adv))
self.gen_o = tf.transpose(
self.gen_o.stack(),
perm=[1, 0, 2]) # batch_size x seq_len x vocab_size
self.gen_x = tf.transpose(self.gen_x.stack(), perm=[1, 0])
self.gen_x_onehot_adv = tf.transpose(self.gen_x_onehot_adv.stack(),
perm=[1, 0, 2])
temp_list = tf.constant(list(range(100)))
temp_list = temp_list[:max_len] - 1
temp_list = tf.tile(tf.reshape(temp_list, [1, max_len]),
[BATCH_SIZE, 1])
self.ifequal = tf.equal(tf.to_int32(tf.argmax(self.gen_o, -1)),
tf.ones_like(self.gen_x))
self.ifequal = tf.to_int32(self.ifequal[:, :-1])
self.ifequal = tf.concat(
[self.ifequal,
tf.ones((BATCH_SIZE, 1), tf.int32)], -1)
self.total_length = tf.multiply(
tf.to_int32(self.ifequal),
temp_list) + 10000 * (1 - tf.to_int32(self.ifequal))
self.gen_x_length = tf.reduce_mean(
tf.to_float(tf.reduce_min(self.total_length, -1)))
def __init__(self, args, infer=False):
'''
Initialisation function for the class Model.
Params:
args: Contains arguments required for the Model creation
'''
# If sampling new trajectories, then infer mode
if infer:
# Infer one position at a time
args.batch_size = 1
args.seq_length = 1
# Store the arguments
self.args = args
# TODO: (resolve) Do we need to use a fixed seq_length?
# Input data contains sequence of (x,y) points
self.input_data = tf.placeholder(tf.float32,
[None, args.seq_length, 2])
# target data contains sequences of (x,y) points as well
self.target_data = tf.placeholder(tf.float32,
[None, args.seq_length, 2])
# fraction of nodes to drop when running the graph
self.dropout = tf.placeholder(tf.float32)
# Learning rate
self.lr = tf.Variable(args.learning_rate,
trainable=False,
name="learning_rate")
cells = []
# loop through once for each layer of nodes
for _ in range(args.num_layers):
# Initialize a BasicLSTMCell recurrent unit
# args.rnn_size contains the dimension of the hidden state of the LSTM
cell = rnn.BasicLSTMCell(args.rnn_size, state_is_tuple=True)
# Add dropout for training normalization
cell = rnn.DropoutWrapper(cell,
output_keep_prob=1.0 - self.dropout)
cells.append(cell)
cell = rnn.MultiRNNCell(cells, state_is_tuple=True)
# Store the recurrent unit
self.cell = cell
# Initial cell state of the LSTM (initialised with zeros)
self.initial_state = cell.zero_state(batch_size=args.batch_size,
dtype=tf.float32)
# Output size is the set of parameters (mu, sigma, corr)
output_size = 5 # 2 mu, 2 sigma and 1 corr
# Embedding for the spatial coordinates
with tf.variable_scope("coordinate_embedding"):
# The spatial embedding using a ReLU layer
# Embed the 2D coordinates into embedding_size dimensions
# TODO: (improve) For now assume embedding_size = rnn_size
embedding_w = tf.get_variable("embedding_w",
[2, args.embedding_size])
embedding_b = tf.get_variable("embedding_b", [args.embedding_size])
# Output linear layer
with tf.variable_scope("rnnlm"):
output_w = tf.get_variable(
"output_w", [args.rnn_size, output_size],
initializer=tf.truncated_normal_initializer(stddev=0.01),
trainable=True)
output_b = tf.get_variable(
"output_b", [output_size],
initializer=tf.constant_initializer(0.01),
trainable=True)
# Split inputs according to sequences.
## inputs = tf.split(1, args.seq_length, self.input_data)
inputs = tf.split(self.input_data, args.seq_length, 1)
# Get a list of 2D tensors. Each of size numPoints x 2
inputs = [tf.squeeze(input_, [1]) for input_ in inputs]
# Embed the input spatial points into the embedding space
embedded_inputs = []
for x in inputs:
# Each x is a 2D tensor of size numPoints x 2
# Embedding layer
embedded_x = tf.nn.relu(
tf.add(tf.matmul(x, embedding_w), embedding_b))
embedded_inputs.append(embedded_x)
# Feed the embedded input data, the initial state of the LSTM cell, the recurrent unit to the seq2seq decoder
## outputs, last_state = tf.nn.seq2seq.rnn_decoder(embedded_inputs, self.initial_state, cell, loop_function=None, scope="rnnlm")
outputs, last_state = tf.contrib.legacy_seq2seq.rnn_decoder(
embedded_inputs,
self.initial_state,
cell,
loop_function=None,
scope="rnnlm")
# outputs, last_state = tf.nn.dynamic_rnn(cell, embedded_inputs, initial_state=self.initial_state, scope="rnnlm")
# Concatenate the outputs from the RNN decoder and reshape it to ?xargs.rnn_size
## output = tf.reshape(tf.concat(1, outputs), [-1, args.rnn_size])
output = tf.reshape(tf.concat(outputs, 1), [-1, args.rnn_size])
# Apply the output linear layer
output = tf.nn.xw_plus_b(output, output_w, output_b)
# Store the final LSTM cell state after the input data has been feeded
self.final_state = last_state
# reshape target data so that it aligns with predictions
flat_target_data = tf.reshape(self.target_data, [-1, 2])
# Extract the x-coordinates and y-coordinates from the target data
## [x_data, y_data] = tf.split(1, 2, flat_target_data)
[x_data, y_data] = tf.split(flat_target_data, 2, 1)
def tf_2d_normal(x, y, mux, muy, sx, sy, rho):
'''
Function that implements the PDF of a 2D normal distribution
params:
x : input x points
y : input y points
mux : mean of the distribution in x
muy : mean of the distribution in y
sx : std dev of the distribution in x
sy : std dev of the distribution in y
rho : Correlation factor of the distribution
'''
# eq 3 in the paper
# and eq 24 & 25 in Graves (2013)
# Calculate (x - mux) and (y-muy)
## normx = tf.sub(x, mux)
## normy = tf.sub(y, muy)
normx = tf.subtract(x, mux)
normy = tf.subtract(y, muy)
# Calculate sx*sy
## sxsy = tf.mul(sx, sy)
sxsy = tf.multiply(sx, sy)
# Calculate the exponential factor
## z = tf.square(tf.div(normx, sx)) + tf.square(tf.div(normy, sy)) - 2*tf.div(tf.mul(rho, tf.mul(normx, normy)), sxsy)
z = tf.square(tf.div(normx, sx)) + tf.square(tf.div(normy, sy)) -\
2 * tf.div(tf.multiply(rho, tf.multiply(normx, normy)), sxsy)
negRho = 1 - tf.square(rho)
# Numerator
result = tf.exp(tf.div(-z, 2 * negRho))
# Normalization constant
## denom = 2 * np.pi * tf.mul(sxsy, tf.sqrt(negRho))
denom = 2 * np.pi * tf.multiply(sxsy, tf.sqrt(negRho))
# Final PDF calculation
result = tf.div(result, denom)
self.result = result
return result
# Important difference between loss func of Social LSTM and Graves (2013)
# is that it is evaluated over all time steps in the latter whereas it is
# done from t_obs+1 to t_pred in the former
def get_lossfunc(z_mux, z_muy, z_sx, z_sy, z_corr, x_data, y_data):
'''
Function to calculate given a 2D distribution over x and y, and target data
of observed x and y points
params:
z_mux : mean of the distribution in x
z_muy : mean of the distribution in y
z_sx : std dev of the distribution in x
z_sy : std dev of the distribution in y
z_rho : Correlation factor of the distribution
x_data : target x points
y_data : target y points
'''
# step = tf.constant(1e-3, dtype=tf.float32, shape=(1, 1))
# Calculate the PDF of the data w.r.t to the distribution
result0 = tf_2d_normal(x_data, y_data, z_mux, z_muy, z_sx, z_sy,
z_corr)
# result0_2 = tf_2d_normal(tf.add(x_data, step), y_data, z_mux, z_muy, z_sx, z_sy, z_corr)
# result0_3 = tf_2d_normal(x_data, tf.add(y_data, step), z_mux, z_muy, z_sx, z_sy, z_corr)
# result0_4 = tf_2d_normal(tf.add(x_data, step), tf.add(y_data, step), z_mux, z_muy, z_sx, z_sy, z_corr)
# result0 = tf.div(tf.add(tf.add(tf.add(result0_1, result0_2), result0_3), result0_4), tf.constant(4.0, dtype=tf.float32, shape=(1, 1)))
# result0 = tf.mul(tf.mul(result0, step), step)
# For numerical stability purposes
epsilon = 1e-20
# TODO: (resolve) I don't think we need this as we don't have the inner
# summation
# result1 = tf.reduce_sum(result0, 1, keep_dims=True)
# Apply the log operation
result1 = -tf.log(tf.maximum(result0,
epsilon)) # Numerical stability
# TODO: For now, implementing loss func over all time-steps
# Sum up all log probabilities for each data point
return tf.reduce_sum(result1)
def get_coef(output):
# eq 20 -> 22 of Graves (2013)
# TODO : (resolve) Does Social LSTM paper do this as well?
# the paper says otherwise but this is essential as we cannot
# have negative standard deviation and correlation needs to be between
# -1 and 1
z = output
# Split the output into 5 parts corresponding to means, std devs and corr
## z_mux, z_muy, z_sx, z_sy, z_corr = tf.split(1, 5, z)
z_mux, z_muy, z_sx, z_sy, z_corr = tf.split(z, 5, 1)
# The output must be exponentiated for the std devs
z_sx = tf.exp(z_sx)
z_sy = tf.exp(z_sy)
# Tanh applied to keep it in the range [-1, 1]
z_corr = tf.tanh(z_corr)
return [z_mux, z_muy, z_sx, z_sy, z_corr]
# Extract the coef from the output of the linear layer
[o_mux, o_muy, o_sx, o_sy, o_corr] = get_coef(output)
# Store the output from the model
self.output = output
# Store the predicted outputs
self.mux = o_mux
self.muy = o_muy
self.sx = o_sx
self.sy = o_sy
self.corr = o_corr
# Compute the loss function
lossfunc = get_lossfunc(o_mux, o_muy, o_sx, o_sy, o_corr, x_data,
y_data)
# Compute the cost
self.cost = tf.div(lossfunc, (args.batch_size * args.seq_length))
# Get trainable_variables
tvars = tf.trainable_variables()
# L2 loss
l2 = args.lambda_param * sum(tf.nn.l2_loss(tvar) for tvar in tvars)
self.cost = self.cost + l2
# TODO: (resolve) We are clipping the gradients as is usually done in LSTM
# implementations. Social LSTM paper doesn't mention about this at all
# Calculate gradients of the cost w.r.t all the trainable variables
self.gradients = tf.gradients(self.cost, tvars)
# Clip the gradients if they are larger than the value given in args
grads, _ = tf.clip_by_global_norm(self.gradients, args.grad_clip)
# NOTE: Using RMSprop as suggested by Social LSTM instead of Adam as Graves(2013) does
# optimizer = tf.train.AdamOptimizer(self.lr)
# initialize the optimizer with teh given learning rate
optimizer = tf.train.RMSPropOptimizer(self.lr)
# Train operator
self.train_op = optimizer.apply_gradients(zip(grads, tvars))