def angular_loss_2(y_true, y_pred):
y_pred = K.clip(y_pred, _EPSILON, 1.0 - _EPSILON)
loss = tf.convert_to_tensor(0, dtype=tf.float32)
g = tf.constant(1.0, shape=[1], dtype=tf.float32)
c = tf.constant(4.0, shape=[1], dtype=tf.float32)
d = tf.constant(2.0, shape=[1], dtype=tf.float32)
alpha = tf.constant(45.0, shape=[1], dtype=tf.float32)
losses = []
losses2 = []
for i in range(0, batch_size, 3):
try:
xa = y_pred[i + 0]
xp = y_pred[i + 1]
xn = y_pred[i + 2]
fapn = c * (tf.tan(alpha * K.transpose(xa + xp) * xn)**
2) - d * (g +
tf.tan(alpha)**2) * K.transpose(xa) * xp
losses.append(fapn)
losses2.append(K.transpose(xa) * xn - K.transpose(xa) * xp)
loss = (loss + g + _loss)
except:
continue
loss = K.sum(K.log(1 + 2 * K.sum([K.exp(v) for v in losses])))
loss2 = K.sum(K.log(1 + 2 * K.sum([K.exp(v) for v in losses2])))
loss = loss + 2 * loss2
loss = loss / (batch_size / 3)
zero = tf.constant(0.0, shape=[1], dtype=tf.float32)
return tf.maximum(loss, zero)
def Kget_dists(X):
"""Keras code to compute the pairwise distance matrix for a set of
vectors specifie by the matrix X.
"""
x2 = K.expand_dims(K.sum(K.square(X), axis=1), 1)
dists = x2 + K.transpose(x2) - 2 * K.dot(X, K.transpose(X))
return dists
def fallback_metric(self, y_true, y_pred):
#grab the most confident prediction
predictions = K.max(y_pred, axis=-1)
#fill a tensor with our threshold_value
threshold_tensor = tf.fill(tf.shape(predictions), self.threshold)
#Are we confident in our prediction?
threshold_high = predictions > threshold_tensor
threshold_high = tf.cast(threshold_high, tf.int32)
#Do we have low confidence in our prediction?
threshold_low = predictions <= threshold_tensor
threshold_low = tf.cast(threshold_low, tf.int32)
idx_true = K.argmax(y_true, -1)
idx_pred = K.argmax(y_pred, -1)
#For our confident predictions, compare the top prediction to the label of the true value
high_correct = math_ops.equal(idx_true, idx_pred)
high_correct = tf.cast(high_correct, tf.int32)
#For our less confident predictions, grab the top 2 most confident predictions
_, max_pred = tf.math.top_k(y_pred, k=2)
#Gather the lineages of those top 2 predictions using the transpose of the hierarchy's adjaency matrix because the adjacency only points from ancestor to descendant
lineages = tf.gather(K.transpose(self.hierarchy.A), max_pred)
lineages = K.cast(lineages, tf.int32)
#Grab the first two columns of this matrix
fallback = tf.bitwise.bitwise_and(lineages[:, 0], lineages[:, 1])
#Gather the lineage of the true value
actual = tf.gather(K.transpose(self.hierarchy.A), K.argmax(y_true))
actual = K.cast(actual, tf.int32)
#Multiply the two together
overlap_score = K.batch_dot(fallback, actual)
#Are either of the top 2 predictions in the lineage of the true value? If so, overlap_score should be >1 and we count the result as correct
low_correct = overlap_score > 1
low_correct = tf.cast(low_correct, tf.int32)
low_correct = tf.squeeze(low_correct)
#results for the high confidence predictions
high_accuracy = tf.math.multiply(threshold_high, high_correct)
#results for the low confidence predictions
low_accuracy = tf.math.multiply(threshold_low, low_correct)
# total accuracy vector
correct = high_accuracy + low_accuracy
#return batch accuracy value
return K.mean(K.cast(correct, tf.float32))
def call(self, inputs, **kwargs):
""" student t-distribution, as same as used in t-SNE algorithm.
q_ij = 1/(1+dist(x_i, u_j)^2), then normalize it.
Arguments:
inputs: the variable containing data, shape=(n_samples, n_features)
Return:
q: student's t-distribution, or soft labels for each sample. shape=(n_samples, n_clusters)
"""
q = 1.0 / (1.0 + (K.sum(K.square(K.expand_dims(inputs, axis=1) - self.clusters), axis=2) / self.alpha))
q **= (self.alpha + 1.0) / 2.0
q = K.transpose(K.transpose(q) / K.sum(q, axis=1))
return q
def _compute_carry_and_output(self, x, h_tm1, c_tm1, b):
"""Computes carry and output using split kernels."""
x_i, x_f, x_c, x_o = x
h_tm1_i, h_tm1_f, h_tm1_c, h_tm1_o = h_tm1
b_i2, b_f2, b_c2, b_o2 = b
i = self.recurrent_activation(
x_i + K.bias_add(K.dot(h_tm1_i, K.transpose(self.recurrent_kernel[:, :self.units])), b_i2))
f = self.recurrent_activation(x_f + K.bias_add(K.dot(
h_tm1_f, K.transpose(self.recurrent_kernel[:, self.units:self.units * 2])), b_f2))
c = f * c_tm1 + i * self.activation(x_c + K.bias_add(K.dot(
h_tm1_c, K.transpose(self.recurrent_kernel[:, self.units * 2:self.units * 3])), b_c2))
o = self.recurrent_activation(
x_o + K.bias_add(K.dot(h_tm1_o, K.transpose(self.recurrent_kernel[:, self.units * 3:])), b_o2))
return c, o
def call(self, inputs, **kwargs):
main_input, embedding_matrix = inputs
input_shape_tensor = K.shape(main_input)
last_input_dim = K.int_shape(main_input)[-1]
emb_input_dim, emb_output_dim = K.int_shape(embedding_matrix)
projected = K.dot(K.reshape(main_input, (-1, last_input_dim)),
self.embedding_weights['projection'])
if self.add_biases:
projected = K.bias_add(projected,
self.embedding_weights['biases'],
data_format='channels_last')
if 0 < self.projection_dropout < 1:
projected = K.in_train_phase(
lambda: K.dropout(projected, self.projection_dropout),
projected,
training=kwargs.get('training'))
attention = K.dot(projected, K.transpose(embedding_matrix))
if self.scaled_attention:
# scaled dot-product attention, described in
# "Attention is all you need" (https://arxiv.org/abs/1706.03762)
sqrt_d = K.constant(math.sqrt(emb_output_dim), dtype=K.floatx())
attention = attention / sqrt_d
result = K.reshape(
self.activation(attention),
(input_shape_tensor[0], input_shape_tensor[1], emb_input_dim))
return result
def call(self, inputs, training=None):
def _l2normalize(v, eps=1e-12):
return v / (K.sum(v**2)**0.5 + eps)
def power_iteration(W, u):
_u = u
_v = _l2normalize(K.dot(_u, K.transpose(W)))
_u = _l2normalize(K.dot(_v, W))
return _u, _v
if self.spectral_normalization:
W_shape = self.kernel.shape.as_list()
# Flatten the Tensor
W_reshaped = K.reshape(self.kernel, [-1, W_shape[-1]])
_u, _v = power_iteration(W_reshaped, self.u)
# Calculate Sigma
sigma = K.dot(_v, W_reshaped)
sigma = K.dot(sigma, K.transpose(_u))
# normalize it
W_bar = W_reshaped / sigma
# reshape weight tensor
if training in {0, False}:
W_bar = K.reshape(W_bar, W_shape)
else:
with tf.control_dependencies([self.u.assign(_u)]):
W_bar = K.reshape(W_bar, W_shape)
# update weitht
self.kernel = W_bar
if self.rank == 1:
outputs = K.conv1d(inputs,
self.kernel,
strides=self.strides[0],
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate[0])
if self.rank == 2:
outputs = K.conv2d(inputs,
self.kernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.rank == 3:
outputs = K.conv3d(inputs,
self.kernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.use_bias:
outputs = K.bias_add(outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def gram_matrix(x, norm_by_channels=False):
'''
Returns the Gram matrix of the tensor x.
'''
if K.ndim(x) == 3:
features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
shape = K.shape(x)
C, H, W = shape[0], shape[1], shape[2]
gram = K.dot(features, K.transpose(features))
elif K.ndim(x) == 4:
# Swap from (H, W, C) to (B, C, H, W)
x = K.permute_dimensions(x, (0, 3, 1, 2))
shape = K.shape(x)
B, C, H, W = shape[0], shape[1], shape[2], shape[3]
# Reshape as a batch of 2D matrices with vectorized channels
features = K.reshape(x, K.stack([B, C, H * W]))
# This is a batch of Gram matrices (B, C, C).
gram = K.batch_dot(features, features, axes=2)
else:
raise ValueError(
'The input tensor should be either a 3d (H, W, C) or 4d (B, H, W, C) tensor.'
)
# Normalize the Gram matrix
if norm_by_channels:
denominator = C * H * W # Normalization from Johnson
else:
denominator = H * W # Normalization from Google
gram = gram / K.cast(denominator, x.dtype)
return gram
def shift(shape, stride, anchors):
"""Produce shifted anchors based on shape of the map and stride size.
Args:
shape: Shape to shift the anchors over.
stride: Stride to shift the anchors with over the shape.
anchors: The anchors to apply at each location.
Returns:
shifted anchors
"""
shift_x = (K.arange(0, shape[1], dtype=K.floatx()) +
K.constant(0.5, dtype=K.floatx())) * stride
shift_y = (K.arange(0, shape[0], dtype=K.floatx()) +
K.constant(0.5, dtype=K.floatx())) * stride
shift_x, shift_y = tf.meshgrid(shift_x, shift_y)
shift_x = K.reshape(shift_x, [-1])
shift_y = K.reshape(shift_y, [-1])
shifts = K.stack([shift_x, shift_y, shift_x, shift_y], axis=0)
shifts = K.transpose(shifts)
number_of_anchors = K.shape(anchors)[0]
k = K.shape(shifts)[0] # number of base points = feat_h * feat_w
shifts = K.cast(K.reshape(shifts, [k, 1, 4]), K.floatx())
shifted_anchors = K.reshape(anchors, [1, number_of_anchors, 4]) + shifts
shifted_anchors = K.reshape(shifted_anchors, [k * number_of_anchors, 4])
return shifted_anchors
def call(self, inputs, training=None):
def _l2normalize(v, eps=1e-12):
return v / (K.sum(v ** 2) ** 0.5 + eps)
def power_iteration(W, u):
_u = u
_v = _l2normalize(K.dot(_u, K.transpose(W)))
_u = _l2normalize(K.dot(_v, W))
return _u, _v
W_shape = self.kernel.shape.as_list()
#Flatten the Tensor
W_reshaped = K.reshape(self.kernel, [-1, W_shape[-1]])
_u, _v = power_iteration(W_reshaped, self.u)
#Calculate Sigma
sigma=K.dot(_v, W_reshaped)
sigma=K.dot(sigma, K.transpose(_u))
#normalize it
W_bar = W_reshaped / sigma
#reshape weight tensor
if training in {0, False}:
W_bar = K.reshape(W_bar, W_shape)
else:
with tf.control_dependencies([self.u.assign(_u)]):
W_bar = K.reshape(W_bar, W_shape)
output = K.dot(inputs, W_bar)
if self.use_bias:
output = K.bias_add(output, self.bias, data_format='channels_last')
if self.activation is not None:
output = self.activation(output)
return output
def call(self, inputs, **kwargs):
if not inputs.shape[0]:
return inputs
recurrent_input = ops.convert_to_tensor(inputs)
if not self._mixed_precision_policy.should_cast_variables:
recurrent_input = math_ops.cast(recurrent_input, self.dtype)
batch_size = recurrent_input.shape[0]
# Flatten last two dimensions, but along dimension [2]
flat_recurrent = K.reshape(
K.permute_dimensions(recurrent_input, (0, 2, 1)), (batch_size, -1))
outputs = gen_math_ops.mat_mul(
flat_recurrent,
tf.math.multiply(self.recurrent_kernel, self.recurrent_mask))
if self.use_bias:
outputs = nn.bias_add(outputs, self.bias)
if self.activation is not None:
outputs = self.activation(outputs)
# Transform back outputs to original shape
outputs = K.reshape(
K.transpose(outputs),
(self.target_shape[0], self.target_shape[1], batch_size))
outputs = K.reshape(
outputs, (self.target_shape[1], self.target_shape[0], batch_size))
outputs = K.permute_dimensions(outputs, (2, 1, 0))
return outputs
def select_best_leaf(self, y_pred):
if self.N > self.num_leaves:
# if there are more leaf nodes than total nodes in the hierarchy (should always be the case,
# but allowed to work either way) then pad with a zero for each non-leaf node in the taxonomy
y_pred = self._pad(y_pred)
# propagate the probabilities (algo 1)
propagated_probabilities = K.transpose(
K.dot(self.A, K.transpose(y_pred)))
# grab the mask vector for root and repeat it <batch size> times
root = K.repeat(self.root, K.shape(y_pred)[0])
# reshape into (<batch size>, N)
predictions = K.reshape(root, (K.shape(y_pred)[0], ))
# each branch will walk futher out toward leaf nodes (and loops on leaf nodes)
for _ in range(self.depth):
predictions = self._branch(propagated_probabilities, predictions)
return predictions
def power_iteration(self, u, W):
'''
Accroding the paper, we only need to do power iteration one time.
'''
v = self._l2normalize(K.dot(u, K.transpose(W)))
u = self._l2normalize(K.dot(v, W))
return u, v
def call(self, inputs, states, training=None):
# get the standard hidden state from super
output = super(STTAUCell, self).call(inputs, states)
h_before = output[0]
c = output[1][1]
# the following part modifies the hidden state to create STTAU
# sizes: B = batch size, H = hidden dimension size,
# C = number of centroids
# BxC = BxH & HxC
unnormalized_probs = K.dot(h_before, self.centroid_kernel)
# Gumbel-Softmax sample with (learnt) temperature & unnormalized_probs
q_y = tfp.distributions.RelaxedOneHotCategorical(
self.temperature_weight, unnormalized_probs)
# BxC
y = q_y.sample()
if self.hard_sample is True:
# y_hard is a one-hot vector with BxC
y_hard = tf.cast(tf.one_hot(tf.argmax(y, -1), self.centroids),
y.dtype)
y = tf.stop_gradient(y_hard - y) + y
# BxH = BxC & CxH
h_after = K.dot(y, K.transpose(self.centroid_kernel))
# end of STTAU modification
if 0 < self.dropout + self.recurrent_dropout:
if training is None:
h_after._uses_learning_phase = True
return h_before, [h_after, c]
def build(self, input_shape):
dtype = dtypes.as_dtype(self.dtype or K.floatx())
if not (dtype.is_floating or dtype.is_complex):
raise TypeError('Unable to build `Dense` layer with non-floating point '
'dtype %s' % (dtype,))
input_shape = tensor_shape.TensorShape(input_shape)
if tensor_shape.dimension_value(input_shape[-1]) is None:
raise ValueError('The last dimension of the inputs to `Dense` '
'should be defined. Found `None`.')
last_dim = tensor_shape.dimension_value(input_shape[-1])
self.input_spec = InputSpec(min_ndim=2,
axes={-1: last_dim})
if self.tied_to is not None:
self.kernel = K.transpose(self.tied_to.weights[0])
else:
self.kernel = self.add_weight(
'kernel',
shape=[last_dim, self.units],
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
dtype=self.dtype,
trainable=True)
if self.use_bias:
self.bias = self.add_weight(
'bias',
shape=[self.units,],
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
dtype=self.dtype,
trainable=True)
else:
self.bias = None
self.built = True
def call(self, inputs, output_shape=None):
updates, mask = inputs[0], inputs[1]
mask = tf.cast(mask, 'int32')
input_shape = tf.shape(updates, out_type='int32')
# calculation new shape
if output_shape is None:
output_shape = (input_shape[0], input_shape[1] * self.size[0],
input_shape[2] * self.size[1], input_shape[3])
# calculation indices for batch, height, width and feature maps
one_like_mask = K.ones_like(mask, dtype='int32')
batch_shape = K.concatenate([[input_shape[0]], [1], [1], [1]], axis=0)
batch_range = K.reshape(tf.range(output_shape[0], dtype='int32'),
shape=batch_shape)
b = one_like_mask * batch_range
y = mask // (output_shape[2] * output_shape[3])
x = (mask // output_shape[3]) % output_shape[2]
feature_range = tf.range(output_shape[3], dtype='int32')
f = one_like_mask * feature_range
# transpose indices & reshape update values to one dimension
updates_size = tf.size(updates)
indices = K.transpose(
K.reshape(K.stack([b, y, x, f]), [4, updates_size]))
values = K.reshape(updates, [updates_size])
ret = tf.scatter_nd(indices, values, output_shape)
return ret
def call(self, inputs):
if K.dtype(inputs) != 'int32':
inputs = K.cast(inputs, 'int32')
def _l2normalize(v, eps=1e-12):
return v / (K.sum(v ** 2) ** 0.5 + eps)
def power_iteration(W, u):
#Accroding the paper, we only need to do power iteration one time.
_u = u
_v = _l2normalize(K.dot(_u, K.transpose(W)))
_u = _l2normalize(K.dot(_v, W))
return _u, _v
W_shape = self.embeddings.shape.as_list()
#Flatten the Tensor
W_reshaped = K.reshape(self.embeddings, [-1, W_shape[-1]])
_u, _v = power_iteration(W_reshaped, self.u)
#Calculate Sigma
sigma=K.dot(_v, W_reshaped)
sigma=K.dot(sigma, K.transpose(_u))
#normalize it
W_bar = W_reshaped / sigma
#reshape weight tensor
if training in {0, False}:
W_bar = K.reshape(W_bar, W_shape)
else:
with tf.control_dependencies([self.u.assign(_u)]):
W_bar = K.reshape(W_bar, W_shape)
self.embeddings = W_bar
out = K.gather(self.embeddings, inputs)
return out
def call(self, inputs):
X = inputs[0] # Node features (N x F)
A = inputs[1] # Adjacency matrix (N x N)
outputs = []
for head in range(self.attn_heads):
kernel = self.kernels[head] # W in the paper (F x F")
attention_kernel = self.attn_kernels[
head] # Attention kernel a in the paper (2F" x 1)
# Compute inputs to attention network
features = K.dot(X, kernel) # (N x F")
# Compute feature combinations
# Note: [[a_1], [a_2]]^T [[Wh_i], [Wh_2]] = [a_1]^T [Wh_i] + [a_2]^T [Wh_j]
attn_for_self = K.dot(
features, attention_kernel[0]) # (N x 1), [a_1]^T [Wh_i]
attn_for_neighs = K.dot(
features, attention_kernel[1]) # (N x 1), [a_2]^T [Wh_j]
# Attention head a(Wh_i, Wh_j) = a^T [[Wh_i], [Wh_j]]
dense = attn_for_self + K.transpose(
attn_for_neighs) # (N x N) via broadcasting
# Add nonlinearty
dense = LeakyReLU(alpha=0.2)(dense)
# Mask values before activation (Vaswani et al., 2017)
mask = -10e9 * (1.0 - A)
dense += mask
# Apply softmax to get attention coefficients
dense = K.softmax(dense) # (N x N)
# Apply dropout to features and attention coefficients
dropout_attn = Dropout(self.dropout_rate)(dense) # (N x N)
dropout_feat = Dropout(self.dropout_rate)(features) # (N x F")
# Linear combination with neighbors" features
node_features = K.dot(dropout_attn, dropout_feat) # (N x F")
if self.use_bias:
node_features = K.bias_add(node_features, self.biases[head])
if self.attn_heads_reduction == "concat":
# If "concat", compute the activation here (Eq. 5)
node_features = self.activation(node_features)
# Add output of attention head to final output
outputs.append(node_features)
# Aggregate the heads" output according to the reduction method
if self.attn_heads_reduction == "concat":
output = K.concatenate(outputs) # (N x KF")
else:
output = K.mean(K.stack(outputs), axis=0) # N x F")
output = self.activation(output)
return output
def gram_matrix(x):
assert K.ndim(x) == 3
if K.image_dim_ordering() == "th":
features = K.batch_flatten(x)
else:
features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
gram = K.dot(features, K.transpose(features))
return gram
def gram_matrix(x):
assert K.ndim(x) == 3
if K.image_data_format() == "channels_first":
features = K.batch_flatten(x)
else:
features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
gram = K.dot(features, K.transpose(features))
return gram
def compute_win(self, y_true, y_pred, to_numpy=False):
if self.N > self.num_leaves:
# if there are more leaf nodes than total nodes in the hierarchy (should always be the case,
# but allowed to work either way) then pad with a zero for each non-leaf node in the taxonomy
y_true = self._pad(y_true)
y_pred = self._pad(y_pred)
# propagate the probabilities (algo 1)
propagated_probabilities = K.dot(self.A, K.transpose(y_pred))
# find the index from the actual label
win_idx = self.select_correct_idx(y_true)
# find the mask associated with that label
win_mask = tf.gather(self.W, win_idx)
# win is q . w (algo 2)
win = K.batch_dot(win_mask, K.transpose(propagated_probabilities))
# win is in [0.5,1], remap to [0,1]:
remapped = 2 * (win - 0.5)
if to_numpy:
remapped = K.reshape(remapped, []).numpy()
return remapped
def call(self, x, mask=None):
# print(x[0].shape)
# print(x[1].shape)
# x[0] is Nx2, x[1] is Nx8 onehot, self.centers is 8x2
delta_centers = K.dot(K.transpose(x[1]),
(K.dot(x[1], self.centers) - x[0])) # 8x2
center_counts = K.sum(K.transpose(x[1]), axis=1,
keepdims=True) + 1 # 8x1
delta_centers /= center_counts
new_centers = self.centers - self.alpha * delta_centers
self.add_update((self.centers, new_centers), x)
# self.add_update((self.counter, self.counter + 1), x)
self.result = x[0] - K.dot(x[1], self.centers)
self.result = K.sum(self.result**2, axis=1,
keepdims=True) # / K.dot(x[1], center_counts)
return self.result # Nx1
def custom_loss(y_true, y_pred):
"""Args: y_true -- label vector of shape (batch_size, num_classes)"""
samples_per_cluster = K.transpose(
K.sum(y_true, axis=0, keepdims=True) +
1) # Add 1 to avoid division by zero
centers = K.dot(K.transpose(y_true), features) / samples_per_cluster
center_loss = 0.5 * K.sum(K.square(features - K.dot(y_true, centers)))
center_dot_combinations = K.dot(centers, K.transpose(centers))
center_dot_combinations_normed = K.sqrt(
K.square(center_dot_combinations))
pair_dist = center_dot_combinations / center_dot_combinations_normed
# subtract diagonal of pair_dist which only contains ones
pair_dist = pair_dist - K.eye(num_classes)
pair_dist = pair_dist + 1
pair_dist = K.sum(pair_dist)
island_loss = center_loss + pair_dist
return categorical_crossentropy(y_true, y_pred) + island_loss
def call(self, inputs, **kwargs):
pair1_embed, pair2_embed = inputs
pair1_embed = K.l2_normalize(pair1_embed, axis=-1)
pair2_embed = K.l2_normalize(pair2_embed, axis=-1)
sim = K.dot(pair1_embed, K.transpose(pair2_embed))
sim = tf.linalg.tensor_diag_part(sim)
return sim
def power_iteration(W, u, rounds=1):
'''
Accroding the paper, we only need to do power iteration one time.
'''
_u = u
for i in range(rounds):
_v = _l2normalizer(K.dot(_u, W))
_u = _l2normalizer(K.dot(_v, K.transpose(W)))
W_sn = K.sum(K.dot(_u, W) * _v)
return W_sn, _u, _v
def model(embedding_size, n_a):
# word embedding matrix
#word_vec = Input(shape=(embedding_size), name='Words') # batch, 300
word_vec = tf.constant(answer_emb, name='Words', dtype='float32')
# preprocessing sentences into sentence vectors
sentence = Input(shape=(T, embedding_size), name='Sentences') # batch, 50, 300
sentence_vec = Bidirectional(CuDNNGRU(units=n_a, return_sequences=False), name='Sentence_Vectors')(sentence) # batch, 300
# dot
#product = Dot(axes=-1, normalize=False, name='Matrix')([word_vec, sentence_vec])
product = tf.matmul(word_vec, sentence_vec, transpose_b = True, name = 'Matrix')
key_matrix = K.transpose(product)
model = Model(inputs= sentence, outputs=key_matrix)
return model
def gram_matrix(x):
assert K.ndim(x) == 4
grams = list()
for i in range(self.Batch_Size):
img = x[i, :, :, :]
if K.image_data_format() == 'channels_first':
features = K.batch_flatten(img)
else:
features = K.batch_flatten(
K.permute_dimensions(img, (2, 0, 1)))
grams.append(K.dot(features, K.transpose(features)))
gram = tf.keras.backend.stack(grams)
return gram
def call(self, code_block: Tensor, training=False, **kwargs):
# Note: all layers are wrapped with TimeDistributed, thus the shapes have number of
# [batch size, timesteps (token length), features (1 the subtoken value), Etc]
# each subtoken is considered a timestep
# create a mask of the padding sequence of the input
mask_vector = K.cast(K.equal(code_block, 0), dtype='float32') * -1e7
# mask_vector [batch size, max chunk length, 1]
self.logger.info("mask_vector shape = {}".format(mask_vector.shape))
# code_block = Masking(mask_value=0, )(code_block)
tokens_embedding = self.embedding_layer(code_block)
self.logger.info("Tokens shape = {}".format(tokens_embedding.shape))
# tokens_embedding = [batch_size, max chunk length, embedding_dim]
_, h_t = self.gru_layer(tokens_embedding, training=training)
# h_t = [batch_size, k2)
self.logger.info("h_t shape = {}".format(h_t.shape))
l_feat = self.attention_feature_layer([tokens_embedding, h_t])
self.logger.info("L_feat shape = {}".format(l_feat.shape))
# L_feat = [batch size, token length, k2]
alpha = self.attention_weights_layer([l_feat, mask_vector])
self.logger.info("alpha shape = {}".format(alpha.shape))
# alpha = [batch size, token length] weights over embeddings
# apply the attention to the input embedding
n_hat = K.sum((K.expand_dims(alpha, axis=-1) * tokens_embedding),
axis=1)
self.logger.info("n_hat shape = {}".format(n_hat.shape))
# n_hat = [batch size, embedding dim]
# embedding over all vocabulary
E = self.embedding_layer.layer.embeddings
self.logger.info("E shape = {}".format(E.shape))
# E = [vocabulary size, embedding dim]
# Apply attention to the words over all embeddings
n_hat_E = K.nn.math_ops.tensordot(E,
K.transpose(n_hat),
axes=[[1], [0]])
# n_hat_E = [vocabulary size, token length, batch size]
n_hat_E = K.permute_dimensions(n_hat_E, [2, 1, 0])
self.logger.info("n_hat_E shape = {}".format(n_hat_E.shape))
# n_hat_E = [batch size, token length, vocabulary size]
n = self.softmax_layer(K.bias_add(n_hat_E, self.bias))
self.logger.info("n shape = {}".format(n.shape))
# n = [batch size, vocabulary size] the probability of each token in the vocabulary
return n
def gram_matrix(x):
assert K.ndim(x) == 3
if image_dim_ordering() == 'th':
features = K.batch_flatten(x)
else:
features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
shape = K.shape(x)
C, W, H = (shape[0],shape[1], shape[2])
cf = K.reshape(features ,(C,-1))
gram = K.dot(cf, K.transpose(cf)) / K.cast(C*W*H,dtype='float32')
return gram
def call(self, x, mask=None):
# x[0] is N x feature_dim, x[1] is N x num_classes onehot, self.centers is num_classes x feature_dim
delta_centers = K.dot(
K.transpose(x[1]),
(K.dot(x[1], self.centers) - x[0])) # num_classes x feature_dim
center_counts = K.sum(K.transpose(x[1]), axis=1,
keepdims=True) + 1 # num_classes x 1
delta_centers /= center_counts
new_centers = self.centers - self.alpha * delta_centers
self.add_update((self.centers, new_centers), x)
# self.add_update((self.counter, self.counter + 1), x)
center_loss = x[0] - K.dot(x[1], self.centers)
center_loss = K.sum(self.result**2, axis=1,
keepdims=True) # / K.dot(x[1], center_counts)
pair_dist = K.dot(K.transpose(self.centers), self.centers)
pair_dist = pair_dist - K.dot(self.centers, self.centers)
pair_dist = pair_dist / K.sqrt(K.square(pair_dist))
pair_dist = K.sum(pair_dist, keepdims=True)
self.result = center_loss - pair_dist
return self.result # Nx1
