def inference(self):
"""main computation graph here: 1. embeddding layer, 2.Bi-LSTM layer, 3.concat, 4.FC layer 5.softmax """
#1.get emebedding of words in the sentence
self.embedded_words = tf.nn.embedding_lookup(self.Embedding,self.input_x) #shape:[None,sentence_length,embed_size]
#2. Bi-lstm layer
# define lstm cess:get lstm cell output
lstm_fw_cell=rnn.BasicLSTMCell(self.hidden_size) #forward direction cell
lstm_bw_cell=rnn.BasicLSTMCell(self.hidden_size) #backward direction cell
if self.dropout_keep_prob is not None:
lstm_fw_cell=rnn.DropoutWrapper(lstm_fw_cell,output_keep_prob=self.dropout_keep_prob)
lstm_bw_cell=rnn.DropoutWrapper(lstm_bw_cell,output_keep_prob=self.dropout_keep_prob)
# bidirectional_dynamic_rnn: input: [batch_size, max_time, input_size]
# output: A tuple (outputs, output_states)
# where:outputs: A tuple (output_fw, output_bw) containing the forward and the backward rnn output `Tensor`.
outputs,_=tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell,lstm_bw_cell,self.embedded_words,dtype=tf.float32) #[batch_size,sequence_length,hidden_size] #creates a dynamic bidirectional recurrent neural network
print("outputs:===>",outputs) #outputs:(<tf.Tensor 'bidirectional_rnn/fw/fw/transpose:0' shape=(?, 5, 100) dtype=float32>, <tf.Tensor 'ReverseV2:0' shape=(?, 5, 100) dtype=float32>))
#3. concat output
output_rnn=tf.concat(outputs,axis=2) #[batch_size,sequence_length,hidden_size*2]
self.output_rnn_last=tf.reduce_mean(output_rnn,axis=1) #[batch_size,hidden_size*2] #output_rnn_last=output_rnn[:,-1,:] ##[batch_size,hidden_size*2] #TODO
print("output_rnn_last:", self.output_rnn_last) # <tf.Tensor 'strided_slice:0' shape=(?, 200) dtype=float32>
#4. logits(use linear layer)
with tf.name_scope("output"): #inputs: A `Tensor` of shape `[batch_size, dim]`. The forward activations of the input network.
logits = tf.matmul(self.output_rnn_last, self.W_projection) + self.b_projection # [batch_size,num_classes]
return logits
python类transpose()的实例源码
def feed_network(self,data,keep_prob,chunk_size,n_chunks,dynamic):
# This code is copied from tflearn
sequence_lengths = None
if dynamic:
sequence_lengths = net.calc_seqlenth(data if isinstance(data, tf.Tensor) else tf.stack(data))
batch_size = tf.shape(data)[0]
weight_dropout = tf.nn.dropout(self._layer_weights, keep_prob)
rnn_dropout = rnn.core_rnn_cell.DropoutWrapper(self._gru_cell,output_keep_prob=keep_prob)
# Calculation Begin
input_shape = data.get_shape().as_list()
ndim = len(input_shape)
axis = [1, 0] + list(range(2,ndim))
data = tf.transpose(data,(axis))
sequence = tf.unstack(data)
outputs, states = rnn.static_rnn(rnn_dropout, sequence, dtype=tf.float32, sequence_length = sequence_lengths)
if dynamic:
outputs = tf.transpose(tf.stack(outputs), [1, 0, 2])
output = net.advanced_indexing_op(outputs, sequence_lengths)
else:
output = outputs[-1]
output = tf.add(tf.matmul(output,weight_dropout), self._layer_biases)
return output
def forward(self, x):
length = lambda mx: int(mx.get_shape()[0])
with tf.variable_scope("QRNN/Forward"):
if self.c is None:
# init context cell
self.c = tf.zeros([length(x), self.kernel.size], dtype=tf.float32)
if self.conv_size <= 2:
# x is batch_size x sentence_length x word_length
# -> now, transpose it to sentence_length x batch_size x word_length
_x = tf.transpose(x, [1, 0, 2])
for i in range(length(_x)):
t = _x[i] # t is batch_size x word_length matrix
f, z, o = self.kernel.forward(t)
self._step(f, z, o)
else:
c_f, c_z, c_o = self.kernel.conv(x)
for i in range(length(c_f)):
f, z, o = c_f[i], c_z[i], c_o[i]
self._step(f, z, o)
return self.h
def baseline_forward(self, X, size, n_class):
shape = X.get_shape()
_X = tf.transpose(X, [1, 0, 2]) # batch_size x sentence_length x word_length -> batch_size x sentence_length x word_length
_X = tf.reshape(_X, [-1, int(shape[2])]) # (batch_size x sentence_length) x word_length
seq = tf.split(0, int(shape[1]), _X) # sentence_length x (batch_size x word_length)
with tf.name_scope("LSTM"):
lstm_cell = rnn_cell.BasicLSTMCell(size, forget_bias=1.0)
outputs, states = rnn.rnn(lstm_cell, seq, dtype=tf.float32)
with tf.name_scope("LSTM-Classifier"):
W = tf.Variable(tf.random_normal([size, n_class]), name="W")
b = tf.Variable(tf.random_normal([n_class]), name="b")
output = tf.matmul(outputs[-1], W) + b
return output
def cross_entropy_sequence_loss(logits, targets, sequence_length):
"""Calculates the per-example cross-entropy loss for a sequence of logits and
masks out all losses passed the sequence length.
Args:
logits: Logits of shape `[T, B, vocab_size]`
targets: Target classes of shape `[T, B]`
sequence_length: An int32 tensor of shape `[B]` corresponding
to the length of each input
Returns:
A tensor of shape [T, B] that contains the loss per example, per time step.
"""
with tf.name_scope("cross_entropy_sequence_loss"):
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=targets)
# Mask out the losses we don't care about
loss_mask = tf.sequence_mask(
tf.to_int32(sequence_length), tf.to_int32(tf.shape(targets)[0]))
losses = losses * tf.transpose(tf.to_float(loss_mask), [1, 0])
return losses
def compute_loss(self, decoder_output, _features, labels):
"""Computes the loss for this model.
Returns a tuple `(losses, loss)`, where `losses` are the per-batch
losses and loss is a single scalar tensor to minimize.
"""
#pylint: disable=R0201
# Calculate loss per example-timestep of shape [B, T]
losses = seq2seq_losses.cross_entropy_sequence_loss(
logits=decoder_output.logits[:, :, :],
targets=tf.transpose(labels["target_ids"][:, 1:], [1, 0]),
sequence_length=labels["target_len"] - 1)
# Calculate the average log perplexity
loss = tf.reduce_sum(losses) / tf.to_float(
tf.reduce_sum(labels["target_len"] - 1))
return losses, loss
def get_loss(pred, label, end_points, reg_weight=0.001):
""" pred: B*NUM_CLASSES,
label: B, """
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)
classify_loss = tf.reduce_mean(loss)
tf.summary.scalar('classify loss', classify_loss)
# Enforce the transformation as orthogonal matrix
transform = end_points['transform'] # BxKxK
K = transform.get_shape()[1].value
mat_diff = tf.matmul(transform, tf.transpose(transform, perm=[0,2,1]))
mat_diff -= tf.constant(np.eye(K), dtype=tf.float32)
mat_diff_loss = tf.nn.l2_loss(mat_diff)
tf.summary.scalar('mat loss', mat_diff_loss)
return classify_loss + mat_diff_loss * reg_weight
def get_loss(pred, label, end_points, reg_weight=0.001):
""" pred: BxNxC,
label: BxN, """
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)
classify_loss = tf.reduce_mean(loss)
tf.scalar_summary('classify loss', classify_loss)
# Enforce the transformation as orthogonal matrix
transform = end_points['transform'] # BxKxK
K = transform.get_shape()[1].value
mat_diff = tf.matmul(transform, tf.transpose(transform, perm=[0,2,1]))
mat_diff -= tf.constant(np.eye(K), dtype=tf.float32)
mat_diff_loss = tf.nn.l2_loss(mat_diff)
tf.scalar_summary('mat_loss', mat_diff_loss)
return classify_loss + mat_diff_loss * reg_weight
def get_loss(l_pred, seg_pred, label, seg, weight, end_points):
per_instance_label_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=l_pred, labels=label)
label_loss = tf.reduce_mean(per_instance_label_loss)
# size of seg_pred is batch_size x point_num x part_cat_num
# size of seg is batch_size x point_num
per_instance_seg_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=seg_pred, labels=seg), axis=1)
seg_loss = tf.reduce_mean(per_instance_seg_loss)
per_instance_seg_pred_res = tf.argmax(seg_pred, 2)
# Enforce the transformation as orthogonal matrix
transform = end_points['transform'] # BxKxK
K = transform.get_shape()[1].value
mat_diff = tf.matmul(transform, tf.transpose(transform, perm=[0,2,1])) - tf.constant(np.eye(K), dtype=tf.float32)
mat_diff_loss = tf.nn.l2_loss(mat_diff)
total_loss = weight * seg_loss + (1 - weight) * label_loss + mat_diff_loss * 1e-3
return total_loss, label_loss, per_instance_label_loss, seg_loss, per_instance_seg_loss, per_instance_seg_pred_res
def __init__(self, embedding):
self.sess = tf.Session()
self.inputs = tf.placeholder(tf.float32,
[None, embedding.shape[1]],
name='inputs')
self.test_vec = tf.placeholder(tf.float32, [1, embedding.shape[1]],
name='test_vec')
self.cos_distance = tf.matmul(self.inputs, tf.transpose(self.test_vec))
#-----------------------------------------------------------------------
# Compute normalized embedding matrix
#-----------------------------------------------------------------------
row_sum = tf.reduce_sum(tf.square(self.inputs), axis=1,
keep_dims=True)
norm = tf.sqrt(row_sum)
self.normalized = self.inputs / norm
self.embedding = self.sess.run(self.normalized,
feed_dict={self.inputs: embedding})
#---------------------------------------------------------------------------
def make_png_thumbnail(x, n):
'''
Input:
`x`: Tensor, value range=[-1, 1), shape=[n*n, h, w, c]
`n`: sqrt of the number of images
Return:
`tf.string` (bytes) of the PNG.
(write these binary directly into a file)
'''
with tf.name_scope('MakeThumbnail'):
_, h, w, c = x.get_shape().as_list()
x = tf.reshape(x, [n, n, h, w, c])
x = tf.transpose(x, [0, 2, 1, 3, 4])
x = tf.reshape(x, [n * h, n * w, c])
x = x / 2. + .5
x = tf.image.convert_image_dtype(x, tf.uint8, saturate=True)
x = tf.image.encode_png(x)
return x
def make_png_jet_thumbnail(x, n):
'''
Input:
`x`: Tensor, value range=[-1, 1), shape=[n*n, h, w, c]
`n`: sqrt of the number of images
Return:
`tf.string` (bytes) of the PNG.
(write these binary directly into a file)
'''
with tf.name_scope('MakeThumbnail'):
_, h, w, c = x.get_shape().as_list()
x = tf.reshape(x, [n, n, h, w, c])
x = tf.transpose(x, [0, 2, 1, 3, 4])
x = tf.reshape(x, [n * h, n * w, c])
x = x / 2. + .5
x = gray2jet(x)
x = tf.image.convert_image_dtype(x, tf.uint8, saturate=True)
x = tf.image.encode_png(x)
return x
def repeat(tensor: tf.Tensor, repeats: int, axis: int) -> tf.Tensor:
"""
Repeat elements of the input tensor in the specified axis ``repeats``-times.
.. note::
Chaining of this op may produce TF warnings although the performance seems to be unaffected.
:param tensor: TF tensor to be repeated
:param repeats: number of repeats
:param axis: axis to repeat
:return: tensor with repeated elements
"""
shape = tensor.get_shape().as_list()
dims = np.arange(len(tensor.shape))
prepare_perm = np.hstack(([axis], np.delete(dims, axis)))
restore_perm = np.hstack((dims[1:axis+1], [0], dims[axis+1:]))
indices = tf.cast(tf.floor(tf.range(0, shape[axis]*repeats)/tf.constant(repeats)), 'int32')
shuffled = tf.transpose(tensor, prepare_perm)
repeated = tf.gather(shuffled, indices)
return tf.transpose(repeated, restore_perm)
def get_image_summary(img, idx=0):
"""
Make an image summary for 4d tensor image with index idx
"""
V = tf.slice(img, (0, 0, 0, idx), (1, -1, -1, 1))
V -= tf.reduce_min(V)
V /= tf.reduce_max(V)
V *= 255
img_w = tf.shape(img)[1]
img_h = tf.shape(img)[2]
V = tf.reshape(V, tf.stack((img_w, img_h, 1)))
V = tf.transpose(V, (2, 0, 1))
V = tf.reshape(V, tf.stack((-1, img_w, img_h, 1)))
return V
def recode(self, inputs, skips=None, store=False, **kwargs):
"""
Wrapper around seq_update() to fit recode() signature of parent class.
"""
state = self.get_state()
if 'hidden' in state:
outputs, states = \
self.seq_update(inputs, state['hidden'], skips=skips,
outputs=[state['output']],
states=[], as_list=True, store=store)
else:
outputs, states = \
self.seq_update(inputs, None, skips=skips,
outputs=[inputs[:, :, 0]],
states=[], as_list=True, store=store)
return tf.transpose(tf.stack(outputs[:-1]), perm=[1, 2, 0])
def loss_nce(self,l2_lambda=0.0001): #0.0001-->0.001
"""calculate loss using (NCE)cross entropy here"""
# Compute the average NCE loss for the batch.
# tf.nce_loss automatically draws a new sample of the negative labels each
# time we evaluate the loss.
if self.is_training: #training
#labels=tf.reshape(self.input_y,[-1]) #[batch_size,1]------>[batch_size,]
labels=tf.expand_dims(self.input_y,1) #[batch_size,]----->[batch_size,1]
loss = tf.reduce_mean( #inputs: A `Tensor` of shape `[batch_size, dim]`. The forward activations of the input network.
tf.nn.nce_loss(weights=tf.transpose(self.W_projection),#[hidden_size*2, num_classes]--->[num_classes,hidden_size*2]. nce_weights:A `Tensor` of shape `[num_classes, dim].O.K.
biases=self.b_projection, #[label_size]. nce_biases:A `Tensor` of shape `[num_classes]`.
labels=labels, #[batch_size,1]. train_labels, # A `Tensor` of type `int64` and shape `[batch_size,num_true]`. The target classes.
inputs=self.output_rnn_last,# [batch_size,hidden_size*2] #A `Tensor` of shape `[batch_size, dim]`. The forward activations of the input network.
num_sampled=self.num_sampled, #scalar. 100
num_classes=self.num_classes,partition_strategy="div")) #scalar. 1999
l2_losses = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'bias' not in v.name]) * l2_lambda
loss = loss + l2_losses
return loss
def parseNet(self, net, netstruct, istraining = True):
for key in netstruct:
if key[0] == "conv":
net = self.conv3d(net, key[2], key[1],key[3], key[4])
elif key[0] == "fc":
net = self.fc(net, key[2], key[1], key[3], key[4],activation = key[-1])
elif key[0] == "maxpool":
net = tf.nn.max_pool3d(net, ksize = key[2], strides = key[2], padding = "SAME", name = key[1])
elif key[0] == "dropout" and istraining:
net = tf.nn.dropout(net, key[2], name = key[1])
elif key[0] == "reshape":
net = tf.reshape(net, key[-1])
elif key[0] == "softmax":
net = tf.nn.softmax(net)
elif key[0] == "transpose":
net = tf.transpose(net, perm=key[-1])
return net
def _rnn_attention_decoder(self, decoder_cell, training_wheels):
loop_fn = self._custom_rnn_loop_fn(decoder_cell.output_size, training_wheels=training_wheels)
decoder_outputs, _, (context_vectors_array, attention_logits_array, pointer_probability_array) = \
tf.nn.raw_rnn(decoder_cell,
loop_fn,
swap_memory=True)
decoder_outputs = decoder_outputs.stack()
decoder_outputs = tf.transpose(decoder_outputs, [1, 0, 2])
attention_logits = attention_logits_array.gather(tf.range(0, attention_logits_array.size() - 1))
attention_logits = tf.transpose(attention_logits, [1, 0, 2])
context_vectors = context_vectors_array.gather(tf.range(0, context_vectors_array.size() - 1))
context_vectors = tf.transpose(context_vectors, [1, 0, 2])
pointer_probabilities = pointer_probability_array.gather(tf.range(0, pointer_probability_array.size() - 1))
pointer_probabilities = tf.transpose(pointer_probabilities, [1, 0])
return decoder_outputs, context_vectors, attention_logits, pointer_probabilities
def _score(self, prev_decoder_state, prev_embedding):
# Returns scores in a tensor of shape [batch_size, input_sequence_length]
if self.mode == 'decode':
query_part = self.query_attention_partial_score_placeholder
encoder_part = self.encoder_state_attention_partial_scores_placeholder
else:
query_part = self.query_attention_partial_score
encoder_part = self.encoder_state_attention_partial_scores
embedding_part = tf.matmul(prev_embedding, self.attention_w_e)
output = tf.matmul(prev_decoder_state,
self.attention_w) + embedding_part + query_part + encoder_part + self.attention_b
output = tf.tanh(output)
output = tf.reduce_sum(self.attention_v * output, axis=2)
output = tf.transpose(output, [1, 0])
# Handle input document padding by giving a large penalty, eliminating it from the weighted average
padding_penalty = -1e20 * tf.to_float(1 - tf.sign(self.documents_placeholder))
masked = output + padding_penalty
return masked
def _bbox_transform(self, ex_rois, gt_rois):
ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0
ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0
ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths
ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights
gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0
gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0
gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths
gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights
targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths
targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights
targets_dw = tf.log(gt_widths / ex_widths)
targets_dh = tf.log(gt_heights / ex_heights)
targets = tf.transpose(tf.pack(
(targets_dx, targets_dy, targets_dw, targets_dh),
axis=0
))
return targets
def _cumprod(tensor, axis=0):
"""A custom version of cumprod to prevent NaN gradients when there are zeros in `tensor`
as reported here: https://github.com/tensorflow/tensorflow/issues/3862
:param tensor: tf.Tensor
:return: tf.Tensor
"""
transpose_permutation = None
n_dim = len(tensor.get_shape())
if n_dim > 1 and axis != 0:
if axis < 0:
axis += n_dim
transpose_permutation = np.arange(n_dim)
transpose_permutation[-1], transpose_permutation[0] = 0, axis
tensor = tf.transpose(tensor, transpose_permutation)
def prod(acc, x):
return acc * x
prob = tf.scan(prod, tensor)
tensor = tf.transpose(prob, transpose_permutation)
return tensor
def __init__(self, attention_units, memory, sequence_length=None, time_major=True, mode=0):
self.attention_units = attention_units
self.enc_units = memory.get_shape()[-1].value
if time_major:
memory = tf.transpose(memory, perm=(1, 0, 2))
self.enc_length = tf.shape(memory)[1]
self.batch_size = tf.shape(memory)[0]
self.mode = mode
self.mask = array_ops.sequence_mask(sequence_length, self.enc_length, tf.float32) if sequence_length is not None else None
self.memory = tf.reshape(memory, (tf.shape(memory)[0], self.enc_length, 1, self.enc_units))
# pre-compute Uahj to minimize the computational cost
with tf.variable_scope('attention'):
Ua = tf.get_variable(name='Ua', shape=(1, 1, self.enc_units, self.attention_units))
self.hidden_feats = tf.nn.conv2d(self.memory, Ua, [1, 1, 1, 1], "SAME")
def __init__(self, attention_units, memory, sequence_length=None, time_major=True, mode=0):
self.attention_units = attention_units
self.enc_units = memory.get_shape()[-1].value
if time_major:
memory = tf.transpose(memory, perm=(1,0,2))
self.enc_length = tf.shape(memory)[1]
self.batch_size = tf.shape(memory)[0]
self.mode = mode
self.mask = array_ops.sequence_mask(sequence_length, self.enc_length) if sequence_length is not None else None
self.tiny = -math.inf * tf.ones(shape=(self.batch_size, self.enc_length))
self.memory = tf.reshape(memory, (tf.shape(memory)[0], self.enc_length, 1, self.enc_units))
### pre-compute Uahj to minimize the computational cost
with tf.variable_scope('attention'):
Ua = tf.get_variable(name='Ua', shape=(1, 1, self.enc_units, self.attention_units))
self.hidden_feats = tf.nn.conv2d(self.memory, Ua, [1,1,1,1], "SAME")
def __init__(self, attention_units, memory, time_major=True):
self.attention_units = attention_units
self.enc_units = memory.get_shape()[-1].value
if time_major:
memory = tf.transpose(memory, perm=(1,0,2))
self.enc_length = tf.shape(memory)[1]
self.batch_size = tf.shape(memory)[0]
self.memory = tf.reshape(memory, (tf.shape(memory)[0], self.enc_length, 1, self.enc_units))
# pre-compute Uahj to minimize the computational cost
with tf.variable_scope('attention'):
Ua = tf.get_variable(name='Ua', shape=(1, 1, self.enc_units, self.attention_units),
initializer=gaussian_initializer(mean=0.0, std=0.001))
self.hidden_feats = tf.nn.conv2d(self.memory, Ua, [1,1,1,1], "SAME")
def sampled_softmax_loss(label, logit, projection, num_sampled):
"""
Args:
label:
logit: unscaled log probabilities
projection: (W, b)
num_sampled:
"""
local_label = tf.reshape(label, shape=(-1,1))
local_logit = tf.reshape(logit, shape=(-1, logit.get_shape()[-1].value))
local_Wt = tf.transpose(projection[0], perm=(1,0))
local_b = projection[1]
loss_sum = tf.nn.sampled_softmax_loss(weights=local_Wt, biases=local_b,
labels=local_label,
inputs=local_logit,
num_sampled=num_sampled,
num_classes=local_Wt.get_shape()[0].value)
loss = tf.divide(tf.reduce_sum(loss_sum), tf.cast(tf.size(local_label), dtype=tf.float32))
return loss
def forward(self,z):
if not self.ar:
mu,log_sigma = self._get_mu_and_sigma(z)
else:
# permute z
z = tf.reshape(z,[-1]+[1]*self.hps.z_size)
perm = np.random.permutation(self.hps.z_size)+1
z = tf.transpose(z,np.append([0],perm))
z = tf.reshape(z,[-1,self.hps.z_size])
mu,log_sigma = ar_layer(z,self.hps,n_hidden=self.n_hidden)
log_sigma = tf.clip_by_value(log_sigma,-5,5)
if not self.hps.ignore_sigma_flow:
y = z * tf.exp(log_sigma) + mu
log_det = -1 * log_sigma
else:
y = z + mu
log_det = 0.0
return y,log_det
def _meshgrid(self, height, width):
with tf.variable_scope('_meshgrid'):
# This should be equivalent to:
# x_t, y_t = np.meshgrid(np.linspace(-1, 1, width),
# np.linspace(-1, 1, height))
# ones = np.ones(np.prod(x_t.shape))
# grid = np.vstack([x_t.flatten(), y_t.flatten(), ones])
x_t = tf.matmul(tf.ones(shape=tf.pack([height, 1])),
tf.transpose(tf.expand_dims(tf.linspace(-1.0, 1.0, width), 1), [1, 0]))
y_t = tf.matmul(tf.expand_dims(tf.linspace(-1.0, 1.0, height), 1),
tf.ones(shape=tf.pack([1, width])))
x_t_flat = tf.reshape(x_t, (1, -1))
y_t_flat = tf.reshape(y_t, (1, -1))
ones = tf.ones_like(x_t_flat)
grid = tf.concat(0, [x_t_flat, y_t_flat, ones])
return grid
ops.py 文件源码
项目：Unsupervised-Anomaly-Detection-with-Generative-Adversarial-Networks
作者: xtarx
项目源码
文件源码
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def minibatch_discrimination(input_layer, num_kernels, dim_per_kernel=5, name='minibatch_discrim'):
# batch_size = input_layer.shape[0]
# num_features = input_layer.shape[1]
batch_size = input_layer.get_shape().as_list()[0]
num_features = input_layer.get_shape().as_list()[1]
W = tf.get_variable('W', [num_features, num_kernels * dim_per_kernel],
initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable('b', [num_kernels], initializer=tf.constant_initializer(0.0))
activation = tf.matmul(input_layer, W)
activation = tf.reshape(activation, [batch_size, num_kernels, dim_per_kernel])
tmp1 = tf.expand_dims(activation, 3)
tmp2 = tf.transpose(activation, perm=[1, 2, 0])
tmp2 = tf.expand_dims(tmp2, 0)
abs_diff = tf.reduce_sum(tf.abs(tmp1 - tmp2), reduction_indices=[2])
f = tf.reduce_sum(tf.exp(-abs_diff), reduction_indices=[2])
f = f + b
return f
def feed_network(self,data,keep_prob,chunk_size,n_chunks, dynamic):
# This code is copied from tflearn
sequence_lengths = None
if dynamic:
sequence_lengths = net.calc_seqlenth(data if isinstance(data, tf.Tensor) else tf.stack(data))
batch_size = tf.shape(data)[0]
weight_dropout = tf.nn.dropout(self._layer_weights, keep_prob)
rnn_dropout = rnn.core_rnn_cell.DropoutWrapper(self._lstm_cell,output_keep_prob=keep_prob)
# Calculation Begin
input_shape = data.get_shape().as_list()
ndim = len(input_shape)
axis = [1, 0] + list(range(2,ndim))
data = tf.transpose(data,(axis))
sequence = tf.unstack(data)
outputs, states = rnn.static_rnn(rnn_dropout, sequence, dtype=tf.float32, sequence_length = sequence_lengths)
if dynamic:
outputs = tf.transpose(tf.stack(outputs), [1, 0, 2])
output = net.advanced_indexing_op(outputs, sequence_lengths)
else:
output = outputs[-1]
output = tf.add(tf.matmul(output,weight_dropout), self._layer_biases)
return output
def _embed_sentences(self):
"""Tensorflow implementation of Simple but Tough-to-Beat Baseline"""
# Get word features
word_embeddings = self._get_embedding()
word_feats = tf.nn.embedding_lookup(word_embeddings, self.input)
# Get marginal estimates and scaling term
batch_size = tf.shape(word_feats)[0]
a = tf.pow(10.0, self._get_a_exp())
p = tf.constant(self.marginals, dtype=tf.float32, name='marginals')
q = tf.reshape(
a / (a + tf.nn.embedding_lookup(p, self.input)),
(batch_size, self.mx_len, 1)
)
# Compute initial sentence embedding
z = tf.reshape(1.0 / tf.to_float(self.input_lengths), (batch_size, 1))
S = z * tf.reduce_sum(q * word_feats, axis=1)
# Compute common component
S_centered = S - tf.reduce_mean(S, axis=0)
_, _, V = tf.svd(S_centered, full_matrices=False, compute_uv=True)
self.tf_ccx = tf.stop_gradient(tf.gather(tf.transpose(V), 0))
# Common component removal
ccx = tf.reshape(self._get_common_component(), (1, self.d))
sv = {'embeddings': word_embeddings, 'a': a, 'p': p, 'ccx': ccx}
return S - tf.matmul(S, ccx * tf.transpose(ccx)), sv
