def loss_CCC(seq1, seq2): """ FUNCTION NAME: loss_CCC This function implements the Concordance Correlation Coefficient (CCC) to be used as a loss function to train models. INPUT: ------ -> seq1: tensor with the true output: (num_batches, seq_len, 1) -> seq2: tensor with the predicted output: (num_batches, seq_len, 1) OUTPUT: ------- <- cccLoss: (1 - CCC) computed to be used as a CCC loss """ seq1 = K.squeeze(seq1, axis=-1) seq2 = K.squeeze(seq2, axis=-1) seq1_mean = K.mean(seq1, axis=-1, keepdims=True) seq2_mean = K.mean(seq2, axis=-1, keepdims=True) cov = (seq1-seq1_mean)*(seq2-seq2_mean) seq1_var = K.mean(K.square(seq1-seq1_mean), axis=-1, keepdims=True) seq2_var = K.mean(K.square(seq2-seq2_mean), axis=-1, keepdims=True) CCC = K.constant(2.) * cov / (seq1_var + seq2_var + K.square(seq1_mean - seq2_mean) + K.epsilon()) CCC_loss = K.constant(1.) - CCC return CCC_loss
def merge_pred(mean_pred, max_pred, partV):
mean_pred = K.squeeze(mean_pred, -1)
max_pred = K.squeeze(max_pred, -1)
output = tf.map_fn(lambda x: each_merge(x[0], x[1], partV),
(mean_pred, max_pred),
dtype=tf.float32)
return tf.reshape(output, [-1, 1])
def fpn_classifier_graph(rois, feature_maps, image_shape, pool_size,
num_classes):
"""Builds the computation graph of the feature pyramid network classifier
and regressor heads.
rois: [batch, num_rois, (y1, x1, y2, x2)] Proposal boxes in normalized
coordinates.
feature_maps: List of feature maps from diffent layers of the pyramid,
[P2, P3, P4, P5]. Each has a different resolution.
image_shape: [height, width, depth]
pool_size: The width of the square feature map generated from ROI Pooling.
num_classes: number of classes, which determines the depth of the results
Returns:
logits: [N, NUM_CLASSES] classifier logits (before softmax)
probs: [N, NUM_CLASSES] classifier probabilities
bbox_deltas: [N, (dy, dx, log(dh), log(dw))] Deltas to apply to
proposal boxes
"""
# ROI Pooling
# Shape: [batch, num_boxes, pool_height, pool_width, channels]
x = PyramidROIAlign([pool_size, pool_size],
image_shape,
name="roi_align_classifier")([rois] + feature_maps)
print x.get_shape()
# Two 1024 FC layers (implemented with Conv2D for consistency)
x = TimeDistributed(Conv2D(1024, (pool_size, pool_size), padding="valid"),
name="mrcnn_class_conv1")(x)
x = TimeDistributed(BatchNorm(axis=3), name='mrcnn_class_bn1')(x)
x = Activation('relu')(x)
x = TimeDistributed(Conv2D(1024, (1, 1)), name="mrcnn_class_conv2")(x)
x = TimeDistributed(BatchNorm(axis=3), name='mrcnn_class_bn2')(x)
x = Activation('relu')(x)
shared = Lambda(lambda x: K.squeeze(K.squeeze(x, 4), 3),
name="pool_squeeze")(x)
print shared.get_shape()
# Classifier head
mrcnn_class_logits = TimeDistributed(Dense(num_classes),
name='mrcnn_class_logits')(shared)
mrcnn_probs = TimeDistributed(Activation("softmax"),
name="mrcnn_class")(mrcnn_class_logits)
print mrcnn_class_logits.get_shape()
print mrcnn_probs.get_shape()
# BBox head
# [batch, boxes, num_classes * (dy, dx, log(dh), log(dw))]
x = TimeDistributed(Dense(num_classes * 4, activation='linear'),
name='mrcnn_bbox_fc')(shared)
# Reshape to [batch, boxes, num_classes, (dy, dx, log(dh), log(dw))]
s = tf.shape(x)
mrcnn_bbox = Reshape((s[1], num_classes, 4), name="mrcnn_bbox")(x)
return mrcnn_class_logits, mrcnn_probs, mrcnn_bbox
def rpn_bbox_loss_graph(config, target_bbox, rpn_match, rpn_bbox):
"""Return the RPN bounding box loss graph.
config: the model config object.
target_bbox: [batch, max positive anchors, (dy, dx, log(dh), log(dw))].
Uses 0 padding to fill in unsed bbox deltas.
rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive,
-1=negative, 0=neutral anchor.
rpn_bbox: [batch, anchors, (dy, dx, log(dh), log(dw))]
"""
# Positive anchors contribute to the loss, but negative and
# neutral anchors (match value of 0 or -1) don't.
rpn_match = K.squeeze(rpn_match, -1)
indices = tf.where(K.equal(rpn_match, 1))
# Pick bbox deltas that contribute to the loss
rpn_bbox = tf.gather_nd(rpn_bbox, indices)
# Trim target bounding box deltas to the same length as rpn_bbox.
batch_counts = K.sum(K.cast(K.equal(rpn_match, 1), tf.int32), axis=1)
target_bbox = batch_pack_graph(target_bbox, batch_counts,
config.IMAGES_PER_GPU)
loss = smooth_l1_loss(target_bbox, rpn_bbox)
loss = K.switch(tf.size(loss) > 0, K.mean(loss), tf.constant(0.0))
return loss
def _each(board1, board2):
# 按x排序, 没起作用
board1 = tf.map_fn(lambda x: _sort(x), board1, dtype=tf.float32)
board2 = tf.map_fn(lambda x: _sort(x), board2, dtype=tf.float32)
# # 查看输入x+v值
# if EmbeddingsLayer.debug:
# logging.getLogger().info("--board1\n %s" % (board1))
# logging.getLogger().info("--board2\n %s" % (board2))
board1 = tf.slice(board1, [0, 0, 1], [-1, -1, 1])
board2 = tf.slice(board2, [0, 0, 1], [-1, -1, 1])
board1 = K.squeeze(board1, -1)
board2 = K.squeeze(board2, -1)
# shape (17, 5)
output = tf.map_fn(lambda x: _compare(x[0], x[1]), (board1, board2),
dtype=tf.float32)
return output
def energy_step(inputs, states):
""" Step function for computing energy for a single decoder state """
# input: (batch_size, latent_dim)
assert_msg = "States must be a list. However states {} is of type {}".format(
states, type(states))
assert isinstance(states, list) or isinstance(states,
tuple), assert_msg
""" Computing sj.Ua """
# (batch_size, 1, d3)
U_a_dot_s = K.expand_dims(K.dot(inputs, self.U_a), 1)
if verbose:
print('Ua.h>', K.int_shape(U_a_dot_s))
""" tanh(h.Wa + s.Ua) """
# (batch_size, h1*h2*...*hn, d3) = (batch_size, h1*h2*...*hn, d3) + (batch_size, 1, d3)
Wh_plus_Us = K.tanh(W_hi + U_a_dot_s)
# (batch_size, d3, h1*h2*...*hn)
Wh_plus_Us = K.permute_dimensions(Wh_plus_Us, (0, 2, 1))
if verbose:
print('Wh+Us>', K.int_shape(Wh_plus_Us))
""" softmax(va.tanh(S.Wa + hj.Ua)) """
# (1, batch_size, h1*h2*...*hn) = (1, d3) . (batch_size, d3, h1*h2*...*hn)
Wh_plus_Us_dot_Va = K.dot(self.V_a, Wh_plus_Us)
# (batch_size, h1*h2*...*hn)
e_i = K.squeeze(Wh_plus_Us_dot_Va, 0)
e_i = K.softmax(e_i)
if verbose:
print('ei>', K.int_shape(e_i))
# (batch_size, h1*h2*...*hn)
return e_i, states
def call(self, inputs, training=None):
class_labels = K.squeeze(inputs[1], axis=1)
inputs = inputs[0]
input_shape = K.int_shape(inputs)
reduction_axes = list(range(0, len(input_shape)))
if self.axis is not None:
del reduction_axes[self.axis]
del reduction_axes[0]
normed = inputs
broadcast_shape = [1] * len(input_shape)
broadcast_shape[0] = K.shape(inputs)[0]
if self.axis is not None:
broadcast_shape[self.axis] = input_shape[self.axis]
if self.scale:
broadcast_gamma = K.reshape(K.gather(self.gamma, class_labels), broadcast_shape)
normed = normed * broadcast_gamma
if self.center:
broadcast_beta = K.reshape(K.gather(self.beta, class_labels), broadcast_shape)
normed = normed + broadcast_beta
return normed
def call(self, inputs, **kwargs):
inputs_shape = K.shape(inputs)
mask = K.cast(K.squeeze(K.any(K.not_equal(inputs, 0.),
axis=(-2, -1),
keepdims=True),
axis=-1),
dtype=inputs.dtype)
inputs_to_lstm = K.reshape(inputs,
(-1, inputs.shape[-2], inputs.shape[-1]))
inputs_embed = super(InferenceSpeakerEmbedding,
self).call(inputs_to_lstm)
inputs_embed = K.reshape(
inputs_embed,
(inputs_shape[0], inputs_shape[1], inputs_embed.shape[-1]))
inputs_embed = inputs_embed * mask
n = K.sum(mask, axis=1)
inputs_embed = K.sum(inputs_embed, axis=1) / n
return inputs_embed
def call(self, inputs):
def apply_separate_filter_for_each_batch(inputs):
kernel = inputs[1]
x = K.expand_dims(inputs[0], axis=0)
outputs = K.conv2d(
x,
kernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.bias is not None:
bias = inputs[2]
outputs = K.bias_add(outputs, bias, data_format=self.data_format)
return K.squeeze(outputs, axis=0)
x = inputs[0]
classes = K.squeeze(inputs[1], axis=1)
if self.bias is not None:
outputs = K.map_fn(apply_separate_filter_for_each_batch,
[x, K.gather(self.kernel, classes), K.gather(self.bias, classes)], dtype='float32')
else:
outputs = K.map_fn(apply_separate_filter_for_each_batch,
[x, K.gather(self.kernel, classes)], dtype='float32')
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, inputs):
if self.data_format is None:
data_format = self.data_format
if self.data_format not in {'channels_first', 'channels_last'}:
raise ValueError('Unknown data_format ' + str(data_format))
strides = (1,) + self.strides + (1,)
x = inputs[0]
cls = K.squeeze(inputs[1], axis=-1)
#Kernel preprocess
kernel = K.gather(self.kernel, cls)
#(bs, w, h, c)
kernel = tf.transpose(kernel, [1, 2, 3, 0])
#(w, h, c, bs)
kernel = K.reshape(kernel, (self.kernel_size[0], self.kernel_size[1], -1))
#(w, h, c * bs)
kernel = K.expand_dims(kernel, axis=-1)
#(w, h, c * bs, 1)
if self.data_format == 'channles_first':
x = tf.transpose(x, [0, 2, 3, 1])
bs, w, h, c = K.int_shape(x)
#(bs, w, h, c)
x = tf.transpose(x, [1, 2, 3, 0])
#(w, h, c, bs)
x = K.reshape(x, (w, h, -1))
#(w, h, c * bs)
x = K.expand_dims(x, axis=0)
#(1, w, h, c * bs)
padding = _preprocess_padding(self.padding)
outputs = tf.nn.depthwise_conv2d(x, kernel,
strides=strides,
padding=padding,
rate=self.dilation_rate)
#(1, w, h, c * bs)
_, w, h, _ = K.int_shape(outputs)
outputs = K.reshape(outputs, [w, h, self.filters, -1])
#(w, h, c, bs)
outputs = tf.transpose(outputs, [3, 0, 1, 2])
#(bs, w, h, c)
if self.bias is not None:
#(num_cls, out)
bias = tf.gather(self.bias, cls)
#(bs, bias)
bias = tf.expand_dims(bias, axis=1)
bias = tf.expand_dims(bias, axis=1)
#(bs, bias, 1, 1)
outputs += bias
if self.data_format == 'channles_first':
outputs = tf.transpose(outputs, [0, 3, 1, 2])
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, x, mask=None):
result = self.elmo(
K.squeeze(K.cast(x, tf.string), axis=1),
as_dict=True,
signature='default',
)['default']
return result
def energy_step(inputs, states):
""" Step function for computing energy for a single decoder state
inputs: (batchsize * 1 * de_in_dim)
states: (batchsize * 1 * de_latent_dim)
"""
""" Some parameters required for shaping tensors"""
en_seq_len, en_hidden = encoder_out_seq.shape[
1], encoder_out_seq.shape[2]
de_hidden = inputs.shape[-1]
""" Computing S.Wa where S=[s0, s1, ..., si]"""
# <= batch size * en_seq_len * latent_dim
W_a_dot_s = K.dot(encoder_out_seq, self.W_a)
""" Computing hj.Ua """
U_a_dot_h = K.expand_dims(K.dot(inputs, self.U_a),
1) # <= batch_size, 1, latent_dim
""" tanh(S.Wa + hj.Ua) """
# <= batch_size*en_seq_len, latent_dim
Ws_plus_Uh = K.tanh(W_a_dot_s + U_a_dot_h)
""" softmax(va.tanh(S.Wa + hj.Ua)) """
# <= batch_size, en_seq_len
e_i = K.squeeze(K.dot(Ws_plus_Uh, self.V_a), axis=-1)
# <= batch_size, en_seq_len
e_i = K.softmax(e_i)
return e_i, [e_i]
def soft_min_reg(cv, axis=None, min_disp=None, max_disp=None, labels=None):
if axis == 1:
cv = Lambda(lambda x: K.squeeze(x, axis=-1))(cv)
disp_map = K.reshape(
K.arange(min_disp,
max_disp - 0.000001, (max_disp - min_disp) / labels,
dtype="float32"), (1, 1, labels, 1))
if axis == 1:
output = K.conv2d(cv,
disp_map,
strides=(1, 1),
padding='valid',
data_format="channels_first")
x = K.expand_dims(K.squeeze(output, axis=1), axis=-1)
else:
x = K.conv2d(cv, disp_map, strides=(1, 1), padding='valid')
return x
def add_dcnn_layer(self, x_input, filter_size, kernel_size, stride, padding='valid',
activation='relu', name=None):
x_2d = Lambda(lambda x: K.expand_dims(x, axis=2))(x_input)
x_2d_t = Conv2DTranspose(filters=filter_size, kernel_size=kernel_size, strides=stride,
padding=padding, activation=activation)(x_2d)
x_s = Lambda(lambda w: K.squeeze(w, axis=2), name=name)(x_2d_t)
return x_s
def body(i, max_len, output):
i1 = tf.cast(mi[i], dtype=tf.int32)
i2 = tf.cast(n[i], dtype=tf.int32)
#logging.getLogger().info("--i1,i2,max_len %s %s %s" % (i1,i2,max_len) )
tmp = tf.slice(p, [i1, i2], [1, 1])
tmp = K.squeeze(tmp, 0)
output = tf.concat([output, tmp], 0)
return i + 1, max_len, output
def accuracy(y_true, y_pred):
# reshape in case it's in shape (num_samples, 1) instead of (num_samples,)
if K.ndim(y_true) == K.ndim(y_pred):
y_true = K.squeeze(y_true, -1)
# convert dense predictions to labels
y_pred_labels = K.argmax(y_pred, axis=-1)
y_pred_labels = K.cast(y_pred_labels, K.floatx())
return K.cast(K.equal(y_true, y_pred_labels), K.floatx())
def decode(example):
feature = tf.io.parse_single_example(example, feature_discription)
data = tf.image.decode_jpeg(feature['data'])
data = tf.image.convert_image_dtype(data, tf.float32)
label = feature['label']
label = kbe.one_hot(label, num_classes=10)
label = kbe.squeeze(label, axis=0)
return data, label
def call(self, inputs, mask=None):
'''
:param inputs: a list of tensor of length not larger than 2, or a memory tensor of size BxTXD1.
If a list, the first entry is memory, and the second one is query tensor of size BxD2 if any
:param mask: the masking entry will be directly discarded
:return: a tensor of size BxD1, weighted summing along the sequence dimension
'''
if isinstance(inputs, list) and len(inputs) == 2:
memory, query = inputs
if self.method is None:
return memory[:, -1, :]
elif self.method == 'cba':
hidden = K.dot(memory, self.Wh) + K.expand_dims(K.dot(query, self.Wq), 1)
hidden = K.tanh(hidden)
s = K.squeeze(K.dot(hidden, self.v), -1)
elif self.method == 'ga':
s = K.sum(K.expand_dims(K.dot(query, self.Wq), 1) * memory, axis=-1)
else:
s = K.squeeze(K.dot(memory, self.v), -1)
if mask is not None:
mask = mask[0]
else:
if isinstance(inputs, list):
if len(inputs) != 1:
raise ValueError('inputs length should not be larger than 2')
memory = inputs[0]
else:
memory = inputs
if self.method is None:
return memory[:, -1, :]
elif self.method == 'cba':
hidden = K.dot(memory, self.Wh)
hidden = K.tanh(hidden)
s = K.squeeze(K.dot(hidden, self.v), -1)
elif self.method == 'ga':
raise ValueError('general attention needs the second input')
else:
s = K.squeeze(K.dot(memory, self.v), -1)
s = K.softmax(s)
if mask is not None:
s *= K.cast(mask, dtype='float32')
sum_by_time = K.sum(s, axis=-1, keepdims=True)
s = s / (sum_by_time + K.epsilon())
return K.sum(memory * K.expand_dims(s), axis=1)
def loss(y_true, y_pred):
y_true = K.squeeze(y_true, axis=-1)
# Squeeze y_pred; i.e remove last layer which is of dim 1
y_pred_squeezed = K.squeeze(y_pred, axis=-1)
"""
Reconstructing x_org
"""
# reversed_wnorm = Lambda(lambda x: )
# reversed_wnorm = dict(map(reversed, wnorm.items()))
# x_org = Lambda(lambda x: [tf.reshape(tf.where(tf.equal(wnorm, word)), [-1])[0] for sent in x for word in sent])(y_true)
# x_org =
# x_org = [reversed_wnorm.get(word) for sent in y_true for word in sent]
x_org = raw_input
print(raw_input.shape)
x_temp = Lambda(lambda x: tf.cast(tf.reshape(x, [-1, ]), dtype=tf.int32))(x_org)
K.print_tensor(K.shape(y_pred_squeezed), message='y_pred_squeezed are ')
print(f'Inside decoder....After reshape of x_norm is {y_pred_squeezed.shape}')
# Calc prob logits
print(type(y_pred_squeezed))
print(type(wnorm))
print(f'wnorm shape is {wnorm.shape}')
prob_logits = K.batch_dot(y_pred_squeezed, wnorm, axes=[2, 1])
prob = Lambda(lambda x: tf.nn.log_softmax(x * 100, axis=-1, name='prob_lambda'))(prob_logits)
print(f'Prob shape is {prob.shape}')
prob = Lambda(lambda x: tf.reshape(x, [-1, n_words]))(prob)
# prob = K.reshape(prob, [-1, wnorm.shape[0]])
print(f'Prob reshaped is {prob.shape}')
"""
Get prob of all the words
"""
idx = Lambda(lambda x: tf.range(K.shape(x)[0], K.shape(x)[1]))(y_pred_squeezed)
all_idx = K.transpose(K.stack([idx, x_temp]))
all_prob = Lambda(lambda prob_idx_list: tf.gather_nd(prob_idx_list[0], prob_idx_list[1]))([prob, all_idx])
K.print_tensor(K.shape(all_prob), message='all_prob shape is: ')
recons_loss = Lambda(lambda x: -tf.reduce_mean(x))(all_prob)
# K.print_tensor(loss, message='Loss is: ')
# weighted_recons_loss = loss_weight * recons_loss
return recons_loss
def model(embedding_size, n_a):
X = Input(shape=(T,))
E = Embedding(input_dim=input_dim, output_dim=embedding_size, input_length=T, mask_zero=True, trainable=False)(X)
a1 = Bidirectional(LSTM(units=n_a, return_sequences = True))(E) # functional API needs specifying inputs, just like any functions.
a2 = Dense(16, activation = "tanh")(a1)
yhat = Dense(1, activation = "sigmoid")(a2)
yhat = K.squeeze(yhat, axis=-1)
model = Model(inputs = X, outputs = yhat)
return model
def down_scaling_loop(x1, iterations, i, conv_channels, window_len,
final_shape, max_iterations, b_norm):
# Define parameters for model
kernel_width = min([window_len, 3])
pooling_width = min([window_len, 2])
x_shrink = final_shape[0] - (2**max_iterations) * round(
final_shape[0] / (2**max_iterations)) + 1
if i == 0:
x1 = layers.Conv2D(1, kernel_size=(x_shrink, 1), strides=(1, 1))(x1)
conv_kernel = (kernel_width, 1)
x2 = custom_layers.ReflectionPadding2D(padding=(0, 2))(x1)
x2 = layers.Conv2D(conv_channels[i],
kernel_size=conv_kernel,
dilation_rate=(2, 1))(x2)
x2 = norm_activate(x2, 'relu', b_norm)
x2 = custom_layers.ReflectionPadding2D(padding=(0, 2))(x2)
x3 = layers.Conv2D(conv_channels[i],
kernel_size=conv_kernel,
dilation_rate=(2, 1))(x2)
x3 = norm_activate(x3, 'relu', b_norm)
if iterations > 0:
# Downscale
x_down = layers.Conv2D(conv_channels[i],
kernel_size=(2, 1),
strides=(2, 1),
padding='same')(x3)
x4 = layers.Conv2D(final_shape[-1], kernel_size=(1, 4))(x1)
x4 = norm_activate(x4, 'relu', b_norm)
x_down = down_scaling_loop(x_down, iterations - 1, i + 1,
conv_channels, window_len, final_shape,
max_iterations, b_norm)
x_up = layers.Conv2DTranspose(conv_channels[-1],
kernel_size=conv_kernel,
strides=(pooling_width, 1),
padding='same')(x_down)
x4 = tf.add(x4, x_up)
else:
x4 = layers.Conv2D(conv_channels[i], kernel_size=(1, 4))(x3)
x4 = norm_activate(x4, 'relu', b_norm)
if i == 0:
# Recover original shape
x4 = layers.Conv2DTranspose(final_shape[0],
kernel_size=(x_shrink, 1))(x4)
x4 = k_b.squeeze(x4, axis=2)
return x4
def _concat(p, mi, n, max_len):
output = tf.constant([], dtype=tf.float32)
for i in range(max_len):
i1 = tf.cast(mi[i], dtype=tf.int32)
i2 = tf.cast(n[i], dtype=tf.int32)
tmp = tf.slice(p, [i1, i2], [1, 1])
tmp = K.squeeze(tmp, 0)
output = np.concatenate([output, tmp])
output = tf.cast(output, dtype=tf.float32)
return output
def top_k_accuracy(y_true, y_pred, mask_value=-1, k=5):
""" Top-K Accuracy with Masking """
mask = K.squeeze(K.not_equal(y_true, mask_value), axis=-1)
acc = sparse_top_k_categorical_accuracy(tf.boolean_mask(y_true, mask),
tf.boolean_mask(y_pred, mask),
k=k)
# return 0 if result is nan
acc_filtered = tf.cond(tf.is_nan(acc), lambda: tf.constant(0, tf.float32),
lambda: acc)
return acc_filtered
def call(self, inputs, **kwargs):
pair1, pair2 = inputs
pair1_shape, pair2_shape = K.shape(pair1), K.shape(pair2)
pair1_mask = K.cast(K.squeeze(K.any(K.not_equal(pair1, 0.),
axis=(-2, -1),
keepdims=True),
axis=-1),
dtype=pair1.dtype)
pair2_mask = K.cast(K.squeeze(K.any(K.not_equal(pair2, 0.),
axis=(-2, -1),
keepdims=True),
axis=-1),
dtype=pair2.dtype)
pair1_to_lstm = K.reshape(pair1,
(-1, pair1.shape[-2], pair1.shape[-1]))
pair2_to_lstm = K.reshape(pair2,
(-1, pair2.shape[-2], pair2.shape[-1]))
batch = K.concatenate([pair1_to_lstm, pair2_to_lstm], axis=0)
embedded = super(TestSpeakerEmbedding, self).call(batch)
pair1_embed = embedded[:K.shape(pair1_to_lstm)[0]]
pair2_embed = embedded[K.shape(pair1_to_lstm)[0]:]
pair1_embed = K.reshape(pair1_embed,
(pair1_shape[0], pair1_shape[1], -1))
pair2_embed = K.reshape(pair2_embed,
(pair2_shape[0], pair2_shape[1], -1))
pair1_embed = pair1_embed * pair1_mask
pair2_embed = pair2_embed * pair2_mask
pair1_n = K.sum(pair1_mask, axis=1)
pair2_n = K.sum(pair2_mask, axis=1)
pair1_embed = K.sum(pair1_embed, axis=1) / pair1_n
pair2_embed = K.sum(pair2_embed, axis=1) / pair2_n
return pair1_embed, pair2_embed
def call(self, inputs):
classes = K.squeeze(inputs[1], axis=1)
kernel = K.gather(self.kernel, classes)
#(bs, in, out)
x = K.expand_dims(inputs[0], axis=1)
#(bs, 1, in)
output = tf.matmul(x, kernel)
#(bs, 1, out)
output = K.squeeze(output, axis=1)
#(bs, out)
if self.bias is not None:
b = K.gather(self.bias, classes)
output += b
if self.activation is not None:
return self.activation(output)
return output
def fn(elements):
_embedded_results_ = elements[0]
_categories_size_input_ = K.cast(K.squeeze(elements[1], axis=0), tf.int32)
# 具体原因未知：bug: From merging shape 0 with other shapes. for 'model_2_2/map/while/strided_slice/stack_1' (op: 'Pack') with input shapes: [1], [].
if len(_categories_size_input_.shape) == 1:
_categories_size_input_ = K.cast(K.squeeze(_categories_size_input_, axis=0), tf.int32)
# print('_embedded_results_1',_embedded_results_)
# print('_categories_size_input_', _categories_size_input_)
# def slice2D(x, index):
# return x[150-index:, :]
# embedded_results_ = Lambda(slice2D, arguments={'index':_categories_size_input_})(_embedded_results_) # 切片 2D
_embedded_results_ = _embedded_results_[MAX_SEQUENCE_LENGTH - _categories_size_input_:, :] # 切片 2D
# print('_embedded_results_2',_embedded_results_)
_embedded_results_ = Lambda(lambda x: K.sum(x, axis=0))(_embedded_results_)
# print('_embedded_results_3', _embedded_results_)
return _embedded_results_
def _compare(row1, row2):
### 比较行与行
# shape (5),(5)
# 比较方式: 类型+个数
_sum = tf.constant(0., dtype=tf.float32)
_cnt = tf.constant(0., dtype=tf.float32)
# 类型--两两相乘/总次数
r = 0
while (r < board_size):
c = 0
vr = tf.slice(row1, [r], [1])
vr = K.squeeze(vr, -1)
while (c < board_size):
vc = tf.slice(row2, [c], [1])
vc = K.squeeze(vc, -1)
calc = K.abs(vr + vc) / (K.abs(vr - vc) + K.epsilon())
calc = K.clip(calc, 1, 9) #裁剪
calc = calc - 0.5 + K.abs(vr + vc) * 0.001
_sum = tf.cond(
tf.logical_or(tf.not_equal(vc, 0), tf.not_equal(vr, 0)),
lambda: _sum + calc, lambda: _sum)
_cnt = tf.cond(
tf.logical_or(tf.not_equal(vc, 0), tf.not_equal(vr, 0)),
lambda: _cnt + 1, lambda: _cnt)
c = c + 1
r = r + 1
cnt3 = _compare_cnt(row1, row2)
cnt3 = tf.cond(tf.equal(cnt3, 0), lambda: cnt3 + 1, lambda: cnt3)
output = tf.cond(tf.equal(_cnt, 0), lambda: _sum,
lambda: tf.squeeze(_sum * cnt3 / _cnt))
### 查看行与行比较结果
# if EmbeddingsLayer.debug:
# logging.getLogger().info("\n--row1 %s" % (row1))
# logging.getLogger().info("--row2 %s" % (row2))
# logging.getLogger().info("--sum %s" % (_sum))
# logging.getLogger().info("--cnt %s" % (_cnt))
# logging.getLogger().info("--compare %s" % (output))
###
### 直接输出概率:
### 手动测试0.7比较好
output = output * 0.7
return output
def accuracy(y_true, y_pred, mask_value=-1):
""" Accuracy with Masking """
# mask = K.not_equal(y_true, mask_value)
mask = K.squeeze(K.not_equal(y_true, mask_value), axis=-1)
acc = sparse_categorical_accuracy(tf.boolean_mask(y_true, mask),
tf.boolean_mask(y_pred, mask))
# return 0 if result is empty
res_size = tf.shape(acc)[0]
acc_filtered = tf.cond(tf.equal(res_size, 0),
lambda: tf.constant(0, tf.float32), lambda: acc)
return acc_filtered
def call(self, x, mask=None):
x = K.expand_dims(x, axis=2)
x = tf.nn.separable_conv2d(x,
self.depthwise_w,
self.pointwise_w,
strides=(1, 1, 1, 1),
padding="SAME")
x += self.bias
x = K.relu(x)
outputs = K.squeeze(x, axis=2)
return outputs
def max_singular_val(w, u, fully_differentiable=False, ip=1):
if not fully_differentiable:
w_ = K.stop_gradient(w)
else:
w_ = w
u = K.expand_dims(u, axis=-1)
u_bar = u
for _ in range(ip):
v_bar = tf.matmul(w_, u_bar, transpose_a=True)
v_bar = K.l2_normalize(v_bar, axis=(-1, -2))
u_bar_raw = tf.matmul(w_, v_bar)
u_bar = K.l2_normalize(u_bar_raw, axis=(-1, -2))
sigma = tf.matmul(u_bar, tf.matmul(w, v_bar), transpose_a=True)
sigma = K.squeeze(sigma, axis=-1)
sigma = K.squeeze(sigma, axis=-1)
u_bar = K.squeeze(u_bar, axis=-1)
return sigma, u_bar
