以下代码抛出一个错误。
Traceback (most recent call last):
File "training.py", line 19, in <module>
preds = model.predict(x_test, test_df)
File "D:\brand\models\lstm_detection_model\lstm_brand_detection.py", line 46, in predict
output = [' '.join(np.array(token_df[i])[np.array(ind[i])]) for i in range(len(ind))]
File "D:\brand\models\lstm_detection_model\lstm_brand_detection.py", line 46, in <listcomp>
output = [' '.join(np.array(token_df[i])[np.array(ind[i])]) for i in range(len(ind))]
IndexError: boolean index did not match indexed array along dimension 0; dimension is 16 but corresponding boolean dimension is 17
预测函数:
def predict(self, test_x, test_df=None):
token_df = test_df.apply(word_tokenize)
ind = self.model.predict(test_x, verbose=0).argmax(axis=-1)
ind = [[z for z in obs if z!=2] for obs in ind]
ind = [[False if elem == 0 else True for elem in obs] for obs in ind]
output = [' '.join(np.array(token_df[i])[np.array(ind[i])]) for i in range(len(ind))]
preds = pd.concat([test_df, pd.DataFrame(output, columns=['predictions'])], axis=1)
return preds
这似乎是由于Numpy的更新造成的，有谁知道有什么办法可以纠正这个问题？谢谢!
编辑：把整个LSTM_model文件发了进去。这涉及到模型训练，然后将预测结果输出到一个单独的文件：predictions.csv。这就是错误的地方，训练后。
import numpy as np
import pandas as pd
from nltk import word_tokenize
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers.core import Dropout
from keras.layers.wrappers import Bidirectional
from keras.layers.wrappers import TimeDistributed
from keras.models import Sequential
from crf_layer import ChainCRF
import warnings
warnings.filterwarnings("ignore")
class LstmBrandDetector:
def __init__(self):
self.model = None
def create_model(self, dropout=0.5, units=150):
self.model = Sequential()
self.model.add(Bidirectional(LSTM(units, return_sequences=True),
input_shape=(36, 50)))
self.model.add(Dropout(dropout))
self.model.add(Bidirectional(LSTM(units, return_sequences=True)))
self.model.add(Dropout(dropout))
self.model.add(TimeDistributed(Dense(3)))
self.model.add(Dropout(dropout))
crf = ChainCRF()
self.model.add(crf)
self.model.compile(loss=crf.loss, optimizer='Adam',
metrics=['categorical_accuracy'])
def fit(self, train_x, train_y, epochs=5, batch=28):
self.model.fit(train_x, train_y, epochs=epochs, batch_size=batch)
def save(self, filepath):
self.model.save(filepath)
def print_summary(self):
print(self.model.summary())
def predict(self, test_x, test_df=None):
token_df = test_df.apply(word_tokenize)
ind = self.model.predict(test_x, verbose=0).argmax(axis=-1)
ind = [[z for z in obs if z!=2] for obs in ind]
ind = [[False if elem == 0 else True for elem in obs] for obs in ind]
ind = ind[1:]
output = [' '.join(np.array(token_df[i])[np.array(ind[i])]) for i in range(len(ind))]
preds = pd.concat([test_df, pd.DataFrame(output, columns=['predictions'])], axis=1)
return preds
def evaluate(self, test_x, test_y):
y_pred = self.model.predict(test_x, verbose=0).argmax(axis=-1)
y_test = test_y.argmax(axis=-1)
acc = [np.array_equal(y_pred[i], y_test[i]) for i in
range(len(y_pred))].count(True) / len(y_pred)
return acc
Crf_Layer，它被归纳到BiLSTM模型中。
from __future__ import absolute_import
from keras import backend as K
from keras import initializers
from keras import regularizers
from keras import constraints
from keras.engine import Layer, InputSpec
def path_energy(y, x, U, b_start=None, b_end=None, mask=None):
'''
Calculates the energy of a tag path y for a given input x (with mask),
transition energies U and boundary energies b_start, b_end.
'''
x = add_boundary_energy(x, b_start, b_end, mask)
return path_energy0(y, x, U, mask)
def path_energy0(y, x, U, mask=None):
'''
Path energy without boundary potential handling.
'''
n_classes = K.shape(x)[2]
y_one_hot = K.one_hot(y, n_classes)
energy = K.sum(x * y_one_hot, 2)
energy = K.sum(energy, 1)
y_t = y[:, :-1]
y_tp1 = y[:, 1:]
U_flat = K.reshape(U, [-1])
flat_indices = y_t * n_classes + y_tp1
U_y_t_tp1 = K.gather(U_flat, flat_indices)
if mask is not None:
mask = K.cast(mask, K.floatx())
y_t_mask = mask[:, :-1]
y_tp1_mask = mask[:, 1:]
U_y_t_tp1 *= y_t_mask * y_tp1_mask
energy += K.sum(U_y_t_tp1, axis=1)
return energy
def sparse_chain_crf_loss(y, x, U, b_start=None, b_end=None, mask=None):
'''
Given the true sparsely encoded tag sequence y, input x (with mask),
transition energies U, boundary energies b_start and b_end, it computes
the loss function of a Linear Chain Conditional Random Field:
loss(y, x) = NNL(P(y|x)), where P(y|x) = exp(E(y, x)) / Z.
So, loss(y, x) = - E(y, x) + log(Z)
Here, E(y, x) is the tag path energy, and Z is the normalization constant.
The values log(Z) is also called free energy.
'''
x = add_boundary_energy(x, b_start, b_end, mask)
energy = path_energy0(y, x, U, mask)
energy -= free_energy0(x, U, mask)
return K.expand_dims(-energy, -1)
def chain_crf_loss(y, x, U, b_start=None, b_end=None, mask=None):
'''
Variant of sparse_chain_crf_loss but with one-hot encoded tags y.
'''
y_sparse = K.argmax(y, -1)
y_sparse = K.cast(y_sparse, 'int32')
return sparse_chain_crf_loss(y_sparse, x, U, b_start, b_end, mask)
def add_boundary_energy(x, b_start=None, b_end=None, mask=None):
'''
Given the observations x, it adds the start boundary energy b_start (resp.
end boundary energy b_end on the start (resp. end) elements and multiplies
the mask.
'''
if mask is None:
if b_start is not None:
x = K.concatenate([x[:, :1, :] + b_start, x[:, 1:, :]], axis=1)
if b_end is not None:
x = K.concatenate([x[:, :-1, :], x[:, -1:, :] + b_end], axis=1)
else:
mask = K.cast(mask, K.floatx())
mask = K.expand_dims(mask, 2)
x *= mask
if b_start is not None:
mask_r = K.concatenate([K.zeros_like(mask[:, :1]), mask[:, :-1]],
axis=1)
start_mask = K.cast(K.greater(mask, mask_r), K.floatx())
x = x + start_mask * b_start
if b_end is not None:
mask_l = K.concatenate([mask[:, 1:], K.zeros_like(mask[:, -1:])],
axis=1)
end_mask = K.cast(K.greater(mask, mask_l), K.floatx())
x = x + end_mask * b_end
return x
def viterbi_decode(x, U, b_start=None, b_end=None, mask=None):
'''
Computes the best tag sequence y for a given input x, i.e. the one that
maximizes the value of path_energy.
'''
x = add_boundary_energy(x, b_start, b_end, mask)
alpha_0 = x[:, 0, :]
gamma_0 = K.zeros_like(alpha_0)
initial_states = [gamma_0, alpha_0]
_, gamma = _forward(x,
lambda B: [K.cast(K.argmax(B, axis=1), K.floatx()),
K.max(B, axis=1)],
initial_states,
U,
mask)
y = _backward(gamma, mask)
return y
def free_energy(x, U, b_start=None, b_end=None, mask=None):
'''
Computes efficiently the sum of all path energies for input x, when
runs over all possible tag sequences.
'''
x = add_boundary_energy(x, b_start, b_end, mask)
return free_energy0(x, U, mask)
def free_energy0(x, U, mask=None):
'''
Free energy without boundary potential handling.
'''
initial_states = [x[:, 0, :]]
last_alpha, _ = _forward(x,
lambda B: [K.logsumexp(B, axis=1)],
initial_states,
U,
mask)
return last_alpha[:, 0]
def _forward(x, reduce_step, initial_states, U, mask=None):
'''
Forward recurrence of the linear chain crf.
'''
def _forward_step(energy_matrix_t, states):
alpha_tm1 = states[-1]
new_states = reduce_step(K.expand_dims(alpha_tm1, 2) + energy_matrix_t)
return new_states[0], new_states
U_shared = K.expand_dims(K.expand_dims(U, 0), 0)
if mask is not None:
mask = K.cast(mask, K.floatx())
mask_U = K.expand_dims(K.expand_dims(mask[:, :-1] * mask[:, 1:], 2), 3)
U_shared = U_shared * mask_U
inputs = K.expand_dims(x[:, 1:, :], 2) + U_shared
inputs = K.concatenate([inputs, K.zeros_like(inputs[:, -1:, :, :])],
axis=1)
last, values, _ = K.rnn(_forward_step, inputs, initial_states)
return last, values
def batch_gather(reference, indices):
ref_shape = K.shape(reference)
batch_size = ref_shape[0]
n_classes = ref_shape[1]
flat_indices = K.arange(0, batch_size) * n_classes + K.flatten(indices)
return K.gather(K.flatten(reference), flat_indices)
def _backward(gamma, mask):
'''
Backward recurrence of the linear chain crf.
'''
gamma = K.cast(gamma, 'int32')
def _backward_step(gamma_t, states):
y_tm1 = K.squeeze(states[0], 0)
y_t = batch_gather(gamma_t, y_tm1)
return y_t, [K.expand_dims(y_t, 0)]
initial_states = [K.expand_dims(K.zeros_like(gamma[:, 0, 0]), 0)]
_, y_rev, _ = K.rnn(_backward_step,
gamma,
initial_states,
go_backwards=True)
y = K.reverse(y_rev, 1)
if mask is not None:
mask = K.cast(mask, dtype='int32')
y *= mask
y += -(1 - mask)
return y
class ChainCRF(Layer):
'''
A Linear Chain Conditional Random Field output layer.
It carries the loss function and its weights for computing
the global tag sequence scores. While training it acts as
the identity function that passes the inputs to the subsequently
used loss function. While testing it applies Viterbi decoding
and returns the best scoring tag sequence as one-hot encoded vectors.
# Arguments
init: weight initialization function for chain energies U.
Can be the name of an existing function (str),
or a Theano function (see: [initializers](../initializers.md)).
U_regularizer: instance of [WeightRegularizer](../regularizers.md)
(eg. L1 or L2 regularization), applied to the transition
weight matrix.
b_start_regularizer: instance of [WeightRegularizer]
(../regularizers.md), applied to the start bias b.
b_end_regularizer: instance of [WeightRegularizer](../regularizers.md)
module, applied to the end bias b.
b_start_constraint: instance of the [constraints](../constraints.md)
module, applied to the start bias b.
b_end_constraint: instance of the [constraints](../constraints.md)
module, applied to the end bias b.
weights: list of Numpy arrays for initializing [U, b_start, b_end].
Thus it should be a list of 3 elements of shape
[(n_classes, n_classes), (n_classes, ), (n_classes, )]
'''
def __init__(self, init='glorot_uniform',
U_regularizer=None,
b_start_regularizer=None,
b_end_regularizer=None,
U_constraint=None,
b_start_constraint=None,
b_end_constraint=None,
weights=None,
**kwargs):
super(ChainCRF, self).__init__(**kwargs)
self.init = initializers.get(init)
self.U_regularizer = regularizers.get(U_regularizer)
self.b_start_regularizer = regularizers.get(b_start_regularizer)
self.b_end_regularizer = regularizers.get(b_end_regularizer)
self.U_constraint = constraints.get(U_constraint)
self.b_start_constraint = constraints.get(b_start_constraint)
self.b_end_constraint = constraints.get(b_end_constraint)
self.initial_weights = weights
self.supports_masking = True
self.uses_learning_phase = True
self.input_spec = [InputSpec(ndim=3)]
def compute_output_shape(self, input_shape):
assert input_shape and len(input_shape) == 3
return (input_shape[0], input_shape[1], input_shape[2])
def compute_mask(self, input, mask=None):
if mask is not None:
return K.any(mask, axis=1)
return mask
def _fetch_mask(self):
mask = None
if self.inbound_nodes:
mask = self.inbound_nodes[0].input_masks[0]
return mask
def build(self, input_shape):
assert len(input_shape) == 3
n_classes = input_shape[2]
n_steps = input_shape[1]
assert n_steps is None or n_steps >= 2
self.input_spec = [InputSpec(dtype=K.floatx(),
shape=(None, n_steps, n_classes))]
self.U = self.add_weight(shape=(n_classes, n_classes),
initializer=self.init,
name='U',
regularizer=self.U_regularizer,
constraint=self.U_constraint)
self.b_start = self.add_weight(shape=(n_classes,),
initializer='zero',
name='b_start',
regularizer=self.b_start_regularizer,
constraint=self.b_start_constraint)
self.b_end = self.add_weight(shape=(n_classes,),
initializer='zero',
name='b_end',
regularizer=self.b_end_regularizer,
constraint=self.b_end_constraint)
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
self.built = True
def call(self, x, mask=None):
y_pred = viterbi_decode(x, self.U, self.b_start, self.b_end, mask)
nb_classes = self.input_spec[0].shape[2]
y_pred_one_hot = K.one_hot(y_pred, nb_classes)
return K.in_train_phase(x, y_pred_one_hot)
def loss(self, y_true, y_pred):
'''
Linear Chain Conditional Random Field loss function.
'''
mask = self._fetch_mask()
return chain_crf_loss(y_true, y_pred, self.U, self.b_start, self.b_end,
mask)
def sparse_loss(self, y_true, y_pred):
'''
Linear Chain Conditional Random Field loss function with sparse
tag sequences.
'''
y_true = K.cast(y_true, 'int32')
y_true = K.squeeze(y_true, 2)
mask = self._fetch_mask()
return sparse_chain_crf_loss(y_true, y_pred, self.U, self.b_start,
self.b_end, mask)
def get_config(self):
config = {
'init': initializers.serialize(self.init),
'U_regularizer': regularizers.serialize(self.U_regularizer),
'b_start_regularizer': regularizers.serialize(
self.b_start_regularizer),
'b_end_regularizer': regularizers.serialize(
self.b_end_regularizer),
'U_constraint': constraints.serialize(self.U_constraint),
'b_start_constraint': constraints.serialize(
self.b_start_constraint),
'b_end_constraint': constraints.serialize(self.b_end_constraint)
}
base_config = super(ChainCRF, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def create_custom_objects():
'''
Returns the custom objects, needed for loading a persisted model.
'''
instanceHolder = {'instance': None}
class ClassWrapper(ChainCRF):
def __init__(self, *args, **kwargs):
instanceHolder['instance'] = self
super(ClassWrapper, self).__init__(*args, **kwargs)
def loss(*args):
method = getattr(instanceHolder['instance'], 'loss')
return method(*args)
def sparse_loss(*args):
method = getattr(instanceHolder['instance'], 'sparse_loss')
return method(*args)
return {'ChainCRF': ClassWrapper, 'loss': loss, 'sparse_loss': sparse_loss}
是的，在过去，布尔索引数组可以比它们所索引的对象长；现在它们必须匹配。 这是符合逻辑的，对吧。 以前的行为会让错误的代码运行。
这一行创建了一个列表，即使是 ind 是二维数组，新列表的长度可以不同。
ind = [[z for z in obs if z!=2] for obs in ind]
这只是将这些子列表中的元素变成布尔型的而已
ind = [[False if elem == 0 else True for elem in obs] for obs in ind]
这将布尔指数应用于数组，从 token_df. 而且至少有一个 i，有一个不匹配的长度之间的。np.array(token_df[i]) 和 ind[i]. 鉴于方式的方式 ind 被构造出来，我并不惊讶。
output = [' '.join(np.array(token_df[i])[np.array(ind[i])]) for i in range(len(ind))]
很难想象这样构造布尔索引会得到正确结果的情况，即使长度是正确的。 旧版的 numpy 只是让你用错误的代码过日子，而它应该引发一个错误。
我发现了解决这个问题的方法。
在这一行
output = [' '.join(np.array(token_df[i])[np.array(ind[i])]) for i in range(len(ind))]
把第二个np. array替换成np. where, 尺寸上的不匹配就不存在了.