def aucMetric(true, pred):
#We want strictly 1D arrays - cannot have (batch, 1), for instance
true = (true - K.min(true)) / (K.max(true) - K.min(true))
pred = (pred - K.min(pred)) / (K.max(pred) - K.min(pred))
true = K.flatten(true)
pred = K.flatten(pred)
#total number of elements in this batch
totalCount = K.shape(true)[0]
#sorting the prediction values in descending order
values, indices = tf.nn.top_k(pred, k=totalCount)
#sorting the ground truth values based on the predictions above
sortedTrue = K.gather(true, indices)
#getting the ground negative elements (already sorted above)
negatives = 1 - sortedTrue
#the true positive count per threshold
TPCurve = K.cumsum(sortedTrue)
#area under the curve
auc = K.sum(TPCurve * negatives)
#normalizing the result between 0 and 1
totalCount = K.cast(totalCount, K.floatx())
positiveCount = K.sum(true)
negativeCount = totalCount - positiveCount
totalArea = positiveCount * negativeCount
return auc / totalArea
def to_predict_extractive_dataset(
self, data: pd.DataFrame, **kwargs
) -> Tuple[Union[tf.Tensor, list], list, list, list, Tuple[list, list, list, list]]:
"""
format pandas.DataFrame to create dataset for ExtractiveQA predict
:param data: dataframe with question, context, title and id
:param to_interpret: bool, if data is for BaseInterpret input
:return: context, id, title, last index of each context, (DOI, authors, url, date)
:rtype: tf.Tensor, list, list, list, list, tuple
"""
self.to_predict_dataset(data)
data = data.copy()
data['context'] = data.apply(lambda x: self.to_context(x.question, x.context, "extractive"), axis=1)
data['end_context'] = K.cumsum(data.apply(lambda x:len(x.context), axis=1)).numpy()
context = self.tok_ext.batch_encode_plus(
data.context.explode().to_list(),
truncation = True,
return_tensors = 'tf',
max_length = self.extract_length,
padding = "max_length")['input_ids']
_id, title, end_context, doi, authors, url, date = list(
data[['id', 'title', 'end_context', 'DOI', 'authors', 'URL', 'publication_date']]
.to_dict('list').values())
del data
if kwargs.get('to_interpret', False):
context = list(map(lambda x:K.reshape(x, (1, self.extract_length)), context))
return context, _id, title, end_context, (doi, authors, url, date)
def Mask(self, inputs, seq_len, mode="add"):
"""Mask operation used in multi-head self attention
Args:
seq_len (obj): sequence length of inputs.
mode (str): mode of mask.
Returns:
obj: tensors after masking.
"""
if seq_len == None:
return inputs
else:
mask = K.one_hot(indices=seq_len[:, 0],
num_classes=K.shape(inputs)[1])
mask = 1 - K.cumsum(mask, axis=1)
for _ in range(len(inputs.shape) - 2):
mask = K.expand_dims(mask, 2)
if mode == "mul":
return inputs * mask
elif mode == "add":
return inputs - (1 - mask) * 1e12
def cumsoftmax(x, mode='l2r'):
"""先softmax，然后cumsum，
cumsum区分从左到右、从右到左两种模式
"""
axis = K.ndim(x) - 1
if mode == 'l2r':
x = K.softmax(x, axis=axis)
x = K.cumsum(x, axis=axis)
return x
elif mode == 'r2l':
x = x[..., ::-1]
x = K.softmax(x, axis=axis)
x = K.cumsum(x, axis=axis)
return x[..., ::-1]
else:
return x
def call(self, inputs, states, constants, training=None):
(tgt_curr_input, tgt_pos_input,
dec_mask), dec_output = states[:3], list(states[3:])
enc_output, enc_mask = constants
time = K.max(tgt_pos_input)
col_mask = K.cast(K.equal(K.cumsum(K.ones_like(dec_mask), axis=1),
time),
dtype='int32')
dec_mask = dec_mask + col_mask
tgt_emb = self.o_word_emb(tgt_curr_input)
if self.pos_emb:
tgt_emb = tgt_emb + self.pos_emb(tgt_pos_input, pos_input=True)
x = tgt_emb
xs = []
cc = K.cast(K.expand_dims(col_mask), dtype='float32')
for i, dec_layer in enumerate(self.decoder.layers):
dec_last_state = dec_output[i] * (1 - cc) + tf.einsum(
'ijk,ilj->ilk', x, cc)
x, _, _ = dec_layer(x,
enc_output,
dec_mask,
enc_mask,
dec_last_state=dec_last_state)
xs.append(dec_last_state)
ff_output = self.target_layer(x)
out = K.cast(K.argmax(ff_output, -1), dtype='int32')
return out, [out, tgt_pos_input + 1, dec_mask] + xs
def crps(y_true, y_pred):
y_pred = K.cumsum(y_pred, axis=1)
ym = K.cast(K.reshape(K.argmax(y_true, axis=1) - 99, (-1, 1)),
dtype='int32')
n = K.arange(-99, 100)
step = K.cast(K.greater_equal(n - ym, 0), dtype='float32')
return K.mean(K.sum(K.square(y_pred - step), axis=1)) / 199
def to_predict_abstractive_dataset(self, data):
"""
format pandas.DataFrame to create dataset for AbstractiveQA predict
:param data: dataframe with question, context, title and id
:return: context, id, title and last index of each context
:rtype: tf.Tensor, list, list, list
"""
self.to_predict_dataset(data)
data = data.copy()
data['context'] = data.apply(lambda x: self.to_context(x.question, x.context, True), axis=1)
data['end_context'] = K.cumsum(data.apply(lambda x:len(x.context), axis=1)).numpy()
context = self.tok_abs.batch_encode_plus(
data.context.explode().to_list(),
truncation = True,
return_tensors = 'tf',
max_length = self.encoder_length,
padding = "max_length")['input_ids']
_id, title, end_context, doi, authors, date = list(
data[['id', 'title', 'end_context', 'DOI', 'authors', 'publication_date']]
.to_dict('list').values())
del data
return context, _id, title, end_context, doi, authors, date
def attention(self, pre_q, pre_k, out_seq_len: int, d_model: int,
training=None):
"""
Calculates the output of the attention once the affine transformations
of the inputs are done. Here's the shapes of the arguments:
:param pre_q: (batch_size, q_seq_len, num_heads, d_model // num_heads)
:param pre_k: (batch_size, k_seq_len, num_heads, d_model // num_heads)
:param out_seq_len: the length of the output sequence
:param d_model: dimensionality of the model (by the paper)
:param training: Passed by Keras. Should not be defined manually.
Optional scalar tensor indicating if we're in training
or inference phase.
"""
q_seq_len = K.int_shape(pre_q)[-3]
k_seq_len = K.int_shape(pre_k)[-3]
q_flattened = K.reshape(pre_q, (-1, q_seq_len, d_model))
k_flattened = K.reshape(pre_k, (-1, k_seq_len, d_model))
# projected_k (batch, seq, head, dim)
projected_k = K.reshape(
K.dot(k_flattened, self.k_projection),
(-1, k_seq_len, self.num_heads, d_model//self.num_heads))
# {q,k}_assignments (batch, seq, head)
q_assignments = K.softmax(
K.dot(q_flattened, self.q_assign_weights) + self.q_assign_bias)
k_assignments = K.softmax(
K.dot(k_flattened, self.k_assign_weights) + self.k_assign_bias)
# k_head_contributions (batch, seq, head, dim)
k_head_contributions = projected_k*K.expand_dims(k_assignments, -1)
if self.use_masking:
k_normalization = K.clip(K.cumsum(k_assignments, -2), 1e-3, np.inf)
# agglomerated_values (batch, seq, head, embedding)
agglomerated_values = (K.cumsum(k_head_contributions, -3)/
K.expand_dims(k_normalization, -1))
else:
# agglomerated_values (batch, 1, head, embedding)
k_normalization = K.clip(K.sum(k_assignments, -2, keepdims=True), 1e-3, np.inf)
agglomerated_values = (K.sum(k_head_contributions, -3, keepdims=True)/
K.expand_dims(k_normalization, -1))
result = K.expand_dims(q_assignments, -1)*agglomerated_values
result = K.reshape(result, (-1, q_seq_len, d_model))
result = self.apply_dropout_if_needed(result, training=training)
return K.dot(result, self.output_weights)
def GetSubMask(s):
'''
shape: [B, Q, K], lower triangle because the i-th row should have i 1s.
'''
len_s = tf.shape(s)[1]
bs = tf.shape(s)[:1]
mask = K.cumsum(tf.eye(len_s, batch_shape=bs), 1)
return mask
def get_model():
seq_inp = KL.Input(shape=(14, 2))
x = KL.Conv1D(8, 3, activation="relu")(seq_inp)
x = KL.Conv1D(32, 3, activation="relu", strides=3)(x)
x = KL.Conv1D(128, 3, activation="relu")(x)
x = KL.Flatten()(x)
out1 = KL.Dense(3, activation="relu")(x)
out1 = KL.Lambda(lambda x: K.cumsum(x, axis=1), name=targets[0])(out1)
out2 = KL.Dense(3, activation="relu")(x)
out2 = KL.Lambda(lambda x: K.cumsum(x, axis=1), name=targets[1])(out2)
model = Model(inputs=seq_inp, outputs=[out1, out2])
return model
def positions_func(inputs, pad=0):
"""
A layer filling i-th column of a 2D tensor with
1+ln(1+i) when it contains a meaningful symbol
and with 0 when it contains PAD
"""
position_inputs = K.cumsum(K.ones_like(inputs, dtype="float32"), axis=1)
position_inputs *= K.cast(K.not_equal(inputs, pad), "float32")
return K.log(1.0 + position_inputs)
def call(self, x):
y_embed = K.cumsum(K.ones_like(x[:, :, :, 0]), 1)
x_embed = K.cumsum(K.ones_like(x[:, :, :, 0]), 2)
if self.normalize:
eps = 1e-6
y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
dim_t = K.arange(self.num_pos_feats, dtype='float')
dim = self.temperature ** (2 * (dim_t // 2) /self.num_pos_feats)
pos_x = x_embed[:, :, :, None] / dim_t
pos_y = y_embed[:, :, :, None] / dim_t
pos_x = K.stack((K.sin(pos_x[:, :, :, 0::2]), K.cos(pos_x[:, :, :, 1::2])), axis=4)
b, h, w, d, c = K.int_shape(pos_x)
pos_x = K.reshape(pos_x, (-1, h, w, d*c))
pos_y = K.stack((K.sin(pos_y[:, :, :, 0::2]), K.cos(pos_y[:, :, :, 1::2])), axis=4)
pos_y = K.reshape(pos_y, (-1, h, w, d*c))
pos = K.permute_dimensions(K.concatenate((pos_y, pos_x), axis=3), (0, 3, 1, 2))
return pos
def get_decoder_mask(self_attn_inputs):
"""Returns causal mask to apply for self-attention layer.
Args:
self_attn_inputs: Inputs to self attention layer to determine mask shape
"""
len_s = tf.shape(self_attn_inputs)[1]
bs = tf.shape(self_attn_inputs)[:1]
mask = K.cumsum(tf.eye(len_s, batch_shape=bs), 1)
return mask
def cumax(x, axis=-1):
"""Cumulative sum of softmax activation.
# Arguments
x: Input tensor.
axis: Integer, axis along which the operation is applied.
# Returns
Tensor, output of softmax transformation.
# Raises
ValueError: In case `dim(x) == 1`.
"""
return K.cumsum(activations.softmax(x, axis), axis)
def Mask(self, inputs, seq_len, mode='mul'): #按照seq_len实际长度对inputs进行计算
if seq_len == None:
return inputs
else:
mask = K.one_hot(seq_len[:, 0], K.shape(inputs)[1])
mask = 1 - K.cumsum(mask, 1)
for _ in range(len(inputs.shape) - 2):
mask = K.expand_dims(mask, 2)
if mode == 'mul':
return inputs * mask
if mode == 'add':
return inputs - (1 - mask) * 1e12
def Mask(self, inputs, seq_len, mode="mul"):
if seq_len == None:
return inputs
else:
mask = K.one_hot(seq_len[:, 0], K.shape(inputs)[1])
mask = 1 - K.cumsum(mask, 1)
for _ in range(len(inputs.shape) - 2):
mask = K.expand_dims(mask, 2)
if mode == "mul":
return inputs * mask
if mode == "add":
return inputs - (1 - mask) * 1e12
def compute_copy_loss(self, inputs, mask=None):
_, y_mask, _, y_true, y_pred = inputs
y_mask = tf.cast(y_mask, y_pred.dtype)
y_true = tf.cast(y_true, y_pred.dtype)
y_mask = K.cumsum(y_mask[:, ::-1], axis=1)[:, ::-1]
y_mask = K.cast(K.greater(y_mask, 0.5), K.floatx())
y_mask = y_mask[:, 1:] # mask标记，减少一位
y_pred = y_pred[:, :-1] # 预测序列，错开一位
y_true = y_true[:, :-1] # 预测序列，错开一位
loss = K.sparse_categorical_crossentropy(y_true, y_pred)
loss = K.sum(loss * y_mask) / K.sum(y_mask)
return loss
def negative_hazard_log_likelihood(cs, st, risk):
# sort cs and risk by st
sorting_indices = tf.argsort(st)[::-1]
sorted_cs = tf.gather(cs, sorting_indices) # (?)
sorted_risk = tf.gather(risk, sorting_indices) # (?)
hazard_ratio = K.exp(sorted_risk)
log_risk = K.log(K.cumsum(hazard_ratio))
uncensored_likelihood = sorted_risk - log_risk
censored_likelihood = uncensored_likelihood * sorted_cs
neg_likelihood = -K.sum(censored_likelihood)
return neg_likelihood
def call(self, inputs):
(x, src_seq, enc_output), tgt_embs = inputs[:3], inputs[3:]
enc_mask = K.cast(K.greater(src_seq, 0), 'float32')
llen = tf.shape(tgt_embs[0])[1]
col_mask = K.cast(K.equal(
K.cumsum(K.ones_like(tgt_embs[0], dtype='int32'), axis=1), llen),
dtype='float32')
rs = [x]
for i, dec_layer in enumerate(self.layers):
tgt_emb = tgt_embs[i] + x * col_mask
x, _, _ = dec_layer(x,
enc_output,
enc_mask=enc_mask,
dec_last_state=tgt_emb)
rs.append(x)
return rs
def call(self, x):
if (self.size == None) or (self.mode == 'sum'):
self.size = int(x.shape[-1])
batch_size,seq_len = K.shape(x)[0],K.shape(x)[1]
position_j = 1. / K.pow(10000., \
2 * K.arange(self.size / 2, dtype='float32' \
) / self.size)
position_j = K.expand_dims(position_j, 0)
position_i = K.cumsum(K.ones_like(x[:,:,0]), 1)-1 #K.arange不支持变长，只好用这种方法生成
position_i = K.expand_dims(position_i, 2)
position_ij = K.dot(position_i, position_j)
position_ij = K.concatenate([K.cos(position_ij), K.sin(position_ij)], 2)
if self.mode == 'sum':
return position_ij + x
elif self.mode == 'concat':
return K.concatenate([position_ij, x], 2)
def event_nhll(cs_st_risk):
event_cs = cs_st_risk[0] # (?)
event_st = cs_st_risk[1] # (?)
event_risk = cs_st_risk[2] # (?)
# sort cs by st
sorting_indices = tf.argsort(event_st)[::-1]
sorted_event_cs = tf.gather(event_cs, sorting_indices) # (?)
sorted_event_risk = tf.gather(event_risk, sorting_indices) # (?)
hazard_ratio = K.exp(sorted_event_risk)
log_risk = K.log(K.cumsum(hazard_ratio))
uncensored_likelihood = sorted_event_risk - log_risk
censored_likelihood = uncensored_likelihood * sorted_event_cs
neg_likelihood = -K.sum(censored_likelihood)
return neg_likelihood
def MyWeightedAvg(inputs, binsize, xmin):
ones = K.ones_like(inputs[0, :]) # [1, 1, 1, 1....] (size Nouts)
idx = K.cumsum(ones) # [1, 2, 3, 4....] (size Nouts)
norm = K.sum(
inputs, axis=1, keepdims=True
) # normalization of all outputs by batch. shape is 1D array of size batch (n.b. keepdims=True is critical!)
wsum = K.sum(
idx * inputs, axis=1, keepdims=True
) / norm # array of size batch with weighted avg. of mean in units of bins (n.b. keepdims=True is critical!)
output = (binsize * (wsum - 0.5)
) + xmin # convert from bins to physical units (shape batch,1)
# print('MyWeightedAvg:')
# print(' binsize = %f' % binsize)
# print(' xmin = %f' % xmin)
# print(' input shape = %s' % str(inputs.shape))
# print(' output shape = %s' % str(output.shape))
return output
ret = []
for i in range(len(static)):
dim = static[i]
if dim is None:
dim = shape[i]
ret.append(dim)
return ret
x_val = [[1, 7, 6, 8, 0, 0], [6, 7, 5, 0, 0, 0]]
x = tf.constant(x_val)
len_s = tf.shape(x)[1]
bs = tf.shape(x)[:1]
mask = K.cumsum(tf.eye(len_s, batch_shape=bs), 1)
with tf.Session(config=tf.ConfigProto(log_device_placement=True,
allow_soft_placement=False)) as sess:
init = tf.global_variables_initializer()
sess.run(init)
mask_val = sess.run(mask)
print('Result from K.cumsum (No-GPU supported) = ' + str(mask_val))
ones_part = tf.ones([bs[0], len_s, len_s])
mask = tf.linalg.band_part(ones_part, -1, 0)
with tf.Session(config=tf.ConfigProto(log_device_placement=False,
allow_soft_placement=False)) as sess:
init = tf.global_variables_initializer()
sess.run(init)
mask_val = sess.run(mask)
print('Result from K.range (GPU supported) ' + str(mask_val))
def GetSubMask(s):
len_s = tf.shape(s)[1]
bs = tf.shape(s)[:1]
mask = K.cumsum(tf.eye(len_s, batch_shape=bs), 1)
return mask
def get_pos_seq(self, x):
mask = K.cast(K.not_equal(x, 0), 'int32')
pos = K.cumsum(K.ones_like(x, 'int32'), 1)
return pos * mask
def _build_nrms(self):
hparams = self.hparams
his_input_title = keras.Input(shape=(hparams.his_size,
hparams.doc_size),
dtype="int32")
pred_input_title = keras.Input(shape=(hparams.npratio + 1,
hparams.doc_size),
dtype="int32")
# input_ids = keras.Input(
# shape=(hparams.npratio + 1, hparams.doc_size), dtype="int32"
# )
c_input_masks = keras.Input(shape=(hparams.npratio + 1,
hparams.doc_size),
dtype="int32")
c_segments = keras.Input(shape=(hparams.npratio + 1, hparams.doc_size),
dtype="int32")
c_length = keras.Input(shape=(hparams.npratio + 1, 1), dtype="int32")
h_input_masks = keras.Input(shape=(hparams.his_size, hparams.doc_size),
dtype="int32")
h_segments = keras.Input(shape=(hparams.his_size, hparams.doc_size),
dtype="int32")
h_length = keras.Input(shape=(hparams.his_size, 1), dtype="int32")
one_input_masks = keras.Input(shape=(1, hparams.doc_size),
dtype="int32")
one_segments = keras.Input(shape=(1, hparams.doc_size), dtype="int32")
one_length = keras.Input(shape=(1, 1), dtype="int32")
pred_input_title_one = keras.Input(shape=(
1,
hparams.doc_size,
),
dtype="int32")
pred_title_one_reshape = K.reshape(pred_input_title_one,
(-1, hparams.doc_size))
imp_indexes = keras.Input(shape=(1, ), dtype="int32")
user_indexes = keras.Input(shape=(1, ), dtype="int32")
all_his = keras.Input(shape=(1, ), dtype="int32")
all_can = keras.Input(shape=(1, ), dtype="int32")
embedding_layer = layers.Embedding(
#hparams.word_size,
#hparams.word_emb_dim,
self.word_size,
self.word_emb_dim,
weights=[self.word2vec_embedding],
trainable=True,
)
titleencoder = self._build_newsencoder(embedding_layer)
newsencoder = titleencoder
#=userencoder
#merge_input=
#news_present = layers.TimeDistributed(newsencoder)([pred_input_title,c_input_masks,c_segments])
# pred_input_title_reshape=layers.Reshape((hparams.npratio + 1,hparams.doc_size,))(
# pred_input_title
# )
# c_input_masks_reshape=layers.Reshape((hparams.npratio + 1,1,hparams.doc_size))(
# c_input_masks
# )
# c_segments_reshape=layers.Reshape((hparams.npratio + 1,1,hparams.doc_size))(
# c_segments
# )
pred_input_title_reshape = K.reshape(pred_input_title,
(-1, hparams.doc_size))
print('???pred_input_title_reshape: ', pred_input_title_reshape)
c_input_masks_reshape = K.reshape(c_input_masks,
(-1, hparams.doc_size))
c_segments_reshape = K.reshape(c_segments, (-1, hparams.doc_size))
c_length_reshape = K.reshape(c_length, (-1, 1))
# news_present = newsencoder([pred_input_title_reshape,c_input_masks_reshape,c_segments_reshape])
# print(news_present)
# news_present=layers.Reshape((hparams.npratio + 1,news_present.shape[-1]))(
# news_present
# )
# print(news_present)
# merge_input=keras.layers.concatenate([pred_input_title_reshape,c_input_masks_reshape],axis=2)
# print('???merge_input: ',merge_input)
# merge_input=keras.layers.concatenate([merge_input,c_segments_reshape],axis=2)
# print('???merge_input2: ',merge_input)
# merge_input=keras.Input(shape=(5,hparams.doc_size,), dtype="int32")
# print('???merge_input3: ',merge_input)
news_present1 = newsencoder([
pred_input_title_reshape, c_input_masks_reshape,
c_segments_reshape, c_length_reshape
])
print("???news_present1: ", news_present1)
news_present = K.reshape(
news_present1, (-1, hparams.npratio + 1, news_present1.shape[-1]))
#news_present = layers.TimeDistributed(newsencoder)(merge_input)
print("???news_present2: ", news_present)
userencoder = self._build_userencoder(titleencoder)
user_present = userencoder(
[his_input_title, h_input_masks, h_segments, h_length, all_his])
one_input_masks_reshape = K.reshape(one_input_masks,
(-1, hparams.doc_size))
one_segments_reshape = K.reshape(one_segments, (-1, hparams.doc_size))
one_length_reshape = K.reshape(one_length, (-1, 1))
news_present_one = newsencoder([
pred_title_one_reshape, one_input_masks_reshape,
one_segments_reshape, one_length_reshape
])
preds = layers.Dot(axes=-1)([news_present, user_present])
mask = K.one_hot(indices=all_can[:, 0], num_classes=K.shape(preds)[1])
mask = 1 - K.cumsum(mask, axis=1)
preds = preds - (1 - mask) * 1e12
preds = layers.Activation(activation="softmax")(preds)
pred_one = layers.Dot(axes=-1)([news_present_one, user_present])
pred_one = layers.Activation(activation="sigmoid")(pred_one)
model = keras.Model([
imp_indexes, user_indexes, his_input_title, pred_input_title,
c_input_masks, c_segments, h_input_masks, h_segments, c_length,
h_length, all_his, all_can
],
preds,
name="model")
scorer = keras.Model([
imp_indexes, user_indexes, his_input_title, pred_input_title_one,
one_input_masks, one_segments, h_input_masks, h_segments,
one_length, h_length, all_his, all_can
],
pred_one,
name="score")
# self.test1=keras.Model([imp_indexes, user_indexes, his_input_title, pred_input_title,c_input_masks,c_segments,h_input_masks,h_segments], news_present, name="test")
return model, scorer
if x.get_shape().dims is None:
return tf.shape(x)
static = x.get_shape().as_list()
shape = tf.shape(x)
ret = []
for i in range(len(static)):
dim = static[i]
if dim is None:
dim = shape[i]
ret.append(dim)
return ret
x_val = [[1, 7, 6, 8, 0, 0], [6, 7, 5, 0, 0, 0]]
x = tf.constant(x_val)
pos = K.cumsum(K.ones_like(x, 'int32'), 1)
with tf.Session(config=tf.ConfigProto(log_device_placement=True,
allow_soft_placement=False)) as sess:
init = tf.global_variables_initializer()
sess.run(init)
pos_val = sess.run(pos)
print('Result from K.cumsum (No-GPU supported) = ' + str(pos_val))
tensor_shape = shape_list(x)
print('length = ' + str(tensor_shape[1]))
pos2 = tf.add(tf.range(tensor_shape[1]), 1)
pos2 = tf.tile(pos2, [tensor_shape[0]])
pos2 = tf.reshape(pos2, tensor_shape)
with tf.Session(config=tf.ConfigProto(log_device_placement=False,
allow_soft_placement=False)) as sess:
init = tf.global_variables_initializer()
def EMD_loss(y_true, y_pred, axis):
cdf_ytrue = K.cast(K.cumsum(y_true, axis=axis), dtype=tf.float32)
cdf_ypred = K.cumsum(y_pred, axis=axis)
samplewise_emd = K.sqrt(
K.mean(K.square(K.abs(cdf_ytrue - cdf_ypred)), axis=axis))
return K.mean(samplewise_emd)
def convert_intervals_to_ration_mask(interval):
p = K.cumsum(interval, axis=1)
rations = K.clip(p[:, :, 0] - p[:, :, 1] + interval[:, :, 1], 0, 1)
return rations
def earth_movers_distance(y_true, y_pred):
cdf_true = K.cumsum(y_true, axis=-1)
cdf_pred = K.cumsum(y_pred, axis=-1)
emd = K.sqrt(K.mean(K.square(cdf_true - cdf_pred), axis=-1))
return K.mean(emd)
