本文整理汇总了Python中keras.backend.rnn方法的典型用法代码示例。如果您正苦于以下问题：Python backend.rnn方法的具体用法？Python backend.rnn怎么用？Python backend.rnn使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块keras.backend的用法示例。

在下文中一共展示了backend.rnn方法的16个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: call

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

def call(self, x, mask=None):

input_shape = self.input_spec[0].shape

en_seq = x

x_input = x[:, input_shape[1]-1, :]

x_input = K.repeat(x_input, input_shape[1])

initial_states = self.get_initial_states(x_input)

constants = super(PointerLSTM, self).get_constants(x_input)

constants.append(en_seq)

preprocessed_input = self.preprocess_input(x_input)

last_output, outputs, states = K.rnn(self.step, preprocessed_input,

initial_states,

go_backwards=self.go_backwards,

constants=constants,

input_length=input_shape[1])

return outputs

开发者ID:zygmuntz，项目名称:pointer-networks-experiments，代码行数:20，

示例2: _forward

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

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

开发者ID:UKPLab，项目名称:elmo-bilstm-cnn-crf，代码行数:22，

示例3: _backward

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

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')

# mask output

y *= mask

# set masked values to -1

y += -(1 - mask)

return y

开发者ID:UKPLab，项目名称:elmo-bilstm-cnn-crf，代码行数:25，

示例4: call

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

def call(self, x, mask=None):

input_shape = self.input_spec[0].shape

initial_states = self.get_initial_states(x)

constants = self.get_constants(x)

preprocessed_input = self.preprocess_input(x)

last_output, outputs, states = K.rnn(self.step, preprocessed_input,

initial_states,

go_backwards=False,

mask=mask,

constants=constants,

unroll=False,

input_length=input_shape[1])

if last_output.ndim == 3:

last_output = K.expand_dims(last_output, dim=0)

return last_output

开发者ID:marcellacornia，项目名称:sam，代码行数:20，

示例5: _forward

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

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

开发者ID:kermitt2，项目名称:delft，代码行数:22，

示例6: _backward

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

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')

# mask output

y *= mask

# set masked values to -1

y += -(1 - mask)

return y

开发者ID:kermitt2，项目名称:delft，代码行数:25，

示例7: step

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

def step(self, input_energy_t, states, return_logZ=True):

# not in the following `prev_target_val` has shape = (B, F)

# where B = batch_size, F = output feature dim

# Note: `i` is of float32, due to the behavior of `K.rnn`

prev_target_val, i, chain_energy = states[:3]

t = K.cast(i[0, 0], dtype='int32')

if len(states) > 3:

if K.backend() == 'theano':

m = states[3][:, t:(t + 2)]

else:

m = K.tf.slice(states[3], [0, t], [-1, 2])

input_energy_t = input_energy_t * K.expand_dims(m[:, 0])

chain_energy = chain_energy * K.expand_dims(K.expand_dims(m[:, 0] * m[:, 1])) # (1, F, F)*(B, 1, 1) -> (B, F, F)

if return_logZ:

energy = chain_energy + K.expand_dims(input_energy_t - prev_target_val, 2) # shapes: (1, B, F) + (B, F, 1) -> (B, F, F)

new_target_val = K.logsumexp(-energy, 1) # shapes: (B, F)

return new_target_val, [new_target_val, i + 1]

else:

energy = chain_energy + K.expand_dims(input_energy_t + prev_target_val, 2)

min_energy = K.min(energy, 1)

argmin_table = K.cast(K.argmin(energy, 1), K.floatx()) # cast for tf-version `K.rnn`

return argmin_table, [min_energy, i + 1]

开发者ID:yongyuwen，项目名称:sequence-tagging-ner，代码行数:24，

示例8: call

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

def call(self, x, mask=None, **kwargs):

input_shape = K.int_shape(x)

res = super(ShareableGRU, self).call(x, mask, **kwargs)

self.input_spec = [InputSpec(shape=(self.input_spec[0].shape[0],

None,

self.input_spec[0].shape[2]))]

if K.ndim(x) == K.ndim(res):

# A recent change in Keras

# (https://github.com/fchollet/keras/commit/a9b6bef0624c67d6df1618ca63d8e8141b0df4d0)

# made it so that K.rnn with a tensorflow backend does not retain shape information for

# the sequence length, even if it's present in the input. We need to fix that here so

# that our models have the right shape information. A simple K.reshape is good enough

# to fix this.

result_shape = K.int_shape(res)

if input_shape[1] is not None and result_shape[1] is None:

shape = (input_shape[0] if input_shape[0] is not None else -1,

input_shape[1], result_shape[2])

res = K.reshape(res, shape=shape)

return res

开发者ID:allenai，项目名称:deep_qa，代码行数:21，

示例9: call

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

def call(self, x, mask=None):

# input_shape = (batch_size, input_length, input_dim). This needs to be defined in build.

read_output, initial_memory_states, output_mask = self.read(x, mask)

initial_write_states = self.writer.get_initial_states(read_output) # h_0 and c_0 of the writer LSTM

initial_states = initial_memory_states + initial_write_states

# last_output: (batch_size, output_dim)

# all_outputs: (batch_size, input_length, output_dim)

# last_states:

# last_memory_state: (batch_size, input_length, output_dim)

# last_output

# last_writer_ct

last_output, all_outputs, last_states = K.rnn(self.compose_and_write_step, read_output, initial_states,

mask=output_mask)

last_memory = last_states[0]

if self.return_mode == "last_output":

return last_output

elif self.return_mode == "all_outputs":

return all_outputs

else:

# return mode is output_and_memory

expanded_last_output = K.expand_dims(last_output, dim=1) # (batch_size, 1, output_dim)

# (batch_size, 1+input_length, output_dim)

return K.concatenate([expanded_last_output, last_memory], axis=1)

开发者ID:pdasigi，项目名称:neural-semantic-encoders，代码行数:25，

示例10: call

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

def call(self, x, mask=None):

input_shape = self.input_spec[0].shape

if self.layer.unroll and input_shape[1] is None:

raise ValueError('Cannot unroll a RNN if the '

'time dimension is undefined. \n'

'- If using a Sequential model, '

'specify the time dimension by passing '

'an `input_shape` or `batch_input_shape` '

'argument to your first layer. If your '

'first layer is an Embedding, you can '

'also use the `input_length` argument.\n'

'- If using the functional API, specify '

'the time dimension by passing a `shape` '

'or `batch_shape` argument to your Input layer.')

initial_states = (self.layer.states if self.layer.stateful else

self.layer.get_initial_states(x))

constants = self.get_constants(x)

preprocessed_input = self.layer.preprocess_input(x)

last_output, outputs, states = K.rnn(

self.step, preprocessed_input, initial_states,

go_backwards=self.layer.go_backwards,

mask=mask,

constants=constants,

unroll=self.layer.unroll,

input_length=input_shape[1])

if self.layer.stateful:

updates = []

for i in range(len(states)):

updates.append((self.layer.states[i], states[i]))

self.add_update(updates, x)

return outputs if self.layer.return_sequences else last_output

开发者ID:codekansas，项目名称:gandlf，代码行数:37，

示例11: call

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

def call(self, x, mask=None):

input_shape = self.input_spec[0].shape

# state format: [h(t-1), c(t-1), y(t-1)]

#h_0 = K.zeros_like(x[:, 0, :])

#c_0 = K.zeros_like(x[:, 0, :])

h_0 = K.reshape(x, (-1, self.input_dim))

c_0 = K.reshape(x, (-1, self.input_dim))

initial_states = [h_0, c_0]

#self.states = [None, None]

#initial_states = self.get_initial_states(x)

last_output, outputs, states = K.rnn(step_function=self.step,

inputs=x,

initial_states=initial_states,

go_backwards=self.go_backwards,

mask=mask,

constants=None,

unroll=self.unroll,

input_length=input_shape[1])

if self.return_sequences:

return outputs

else:

return last_output

开发者ID:bnsnapper，项目名称:keras_bn_library，代码行数:29，

示例12: call

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

def call(self, x, mask=None):

# input shape: (nb_samples, time (padded with zeros), input_dim)

# note that the .build() method of subclasses MUST define

# self.input_spec with a complete input shape.

input_shape = self.input_spec[0].shape

if K._BACKEND == 'tensorflow':

if not input_shape[1]:

raise Exception('When using TensorFlow, you should define '

'explicitly the number of timesteps of '

'your sequences.\n'

'If your first layer is an Embedding, '

'make sure to pass it an "input_length" '

'argument. Otherwise, make sure '

'the first layer has '

'an "input_shape" or "batch_input_shape" '

'argument, including the time axis. '

'Found input shape at layer ' + self.name +

': ' + str(input_shape))

if self.layer.stateful:

initial_states = self.layer.states

else:

initial_states = self.layer.get_initial_states(x)

constants = self.get_constants(x)

preprocessed_input = self.layer.preprocess_input(x)

last_output, outputs, states = K.rnn(self.step, preprocessed_input,

initial_states,

go_backwards=self.layer.go_backwards,

mask=mask,

constants=constants,

unroll=self.layer.unroll,

input_length=input_shape[1])

if self.layer.stateful:

self.updates = []

for i in range(len(states)):

self.updates.append((self.layer.states[i], states[i]))

if self.layer.return_sequences:

return outputs

else:

return last_output

开发者ID:saurabhmathur96，项目名称:Neural-Chatbot，代码行数:43，

示例13: call

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

def call(self, x, use_teacher_forcing=True, training=None):

# TODO: check that model is loading from .h5 correctly

# TODO: for now cannot be shared layer

# (only can it we use (or not use) teacher forcing in all cases simultationsly)

# this sequence is used only to extract the amount of timesteps (the same as in output sequence)

fake_input = x

if isinstance(x, list):

# teacher forcing for training

self.x_seq, self.y_true = x

self.use_teacher_forcing = use_teacher_forcing

fake_input = K.expand_dims(self.y_true)

else:

# inference

self.x_seq = x

self.use_teacher_forcing = False

# apply a dense layer over the time dimension of the sequence

# do it here because it doesn't depend on any previous steps

# therefore we can save computation time:

self._uxpb = _time_distributed_dense(self.x_seq, self.U_a, b=self.b_a,

dropout=self.dropout,

input_dim=self.input_dim,

timesteps=self.timesteps,

output_dim=self.units,

training=training)

last_output, outputs, states = K.rnn(

self.step,

inputs=fake_input,

initial_states=self.get_initial_state(self.x_seq)

)

return outputs

开发者ID:asmekal，项目名称:keras-monotonic-attention，代码行数:35，

示例14: step

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

def step(self, input_energy_t, states, return_logZ=True):

# not in the following `prev_target_val` has shape = (B, F)

# where B = batch_size, F = output feature dim

# Note: `i` is of float32, due to the behavior of `K.rnn`

prev_target_val, i, chain_energy = states[:3]

t = K.cast(i[0, 0], dtype='int32')

if len(states) > 3:

if K.backend() == 'theano':

m = states[3][:, t:(t + 2)]

else:

m = K.slice(states[3], [0, t], [-1, 2])

input_energy_t = input_energy_t * K.expand_dims(m[:, 0])

# (1, F, F)*(B, 1, 1) -> (B, F, F)

chain_energy = chain_energy * K.expand_dims(

K.expand_dims(m[:, 0] * m[:, 1]))

if return_logZ:

# shapes: (1, B, F) + (B, F, 1) -> (B, F, F)

energy = chain_energy + K.expand_dims(input_energy_t - prev_target_val, 2)

new_target_val = K.logsumexp(-energy, 1) # shapes: (B, F)

return new_target_val, [new_target_val, i + 1]

else:

energy = chain_energy + K.expand_dims(input_energy_t + prev_target_val, 2)

min_energy = K.min(energy, 1)

# cast for tf-version `K.rnn

argmin_table = K.cast(K.argmin(energy, 1), K.floatx())

return argmin_table, [min_energy, i + 1]

开发者ID:keras-team，项目名称:keras-contrib，代码行数:28，

示例15: test_rnn_no_states

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

def test_rnn_no_states(self):

# implement a simple RNN without states

input_dim = 8

output_dim = 4

timesteps = 5

_, x = parse_shape_or_val((32, timesteps, input_dim))

_, wi = parse_shape_or_val((input_dim, output_dim))

x_k = K.variable(x)

wi_k = K.variable(wi)

def rnn_fn(x_k, h_k):

assert len(h_k) == 0

y_k = K.dot(x_k, wi_k)

return y_k, []

last_y1, y1, h1 = ref_rnn(x, [wi, None, None], None,

go_backwards=False, mask=None)

last_y2, y2, h2 = K.rnn(rnn_fn, x_k, [],

go_backwards=False, mask=None)

assert len(h2) == 0

last_y2 = K.eval(last_y2)

y2 = K.eval(y2)

assert_allclose(last_y1, last_y2, atol=1e-05)

assert_allclose(y1, y2, atol=1e-05)

开发者ID:hello-sea，项目名称:DeepLearning_Wavelet-LSTM，代码行数:30，

示例16: legacy_test_rnn_no_states

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# 需要导入模块: from keras import backend [as 别名]

# 或者: from keras.backend import rnn [as 别名]

def legacy_test_rnn_no_states(self):

# implement a simple RNN without states

input_dim = 8

output_dim = 4

timesteps = 5

input_val = np.random.random((32, timesteps, input_dim))

W_i_val = np.random.random((input_dim, output_dim))

def rnn_step_fn(k):

W_i = k.variable(W_i_val)

def step_function(x, states):

assert len(states) == 0

output = k.dot(x, W_i)

return output, []

return step_function

# test default setup

last_output_list = []

outputs_list = []

for k in BACKENDS:

rnn_fn = rnn_step_fn(k)

inputs = k.variable(input_val)

initial_states = []

last_output, outputs, new_states = k.rnn(rnn_fn, inputs,

initial_states,

go_backwards=False,

mask=None)

last_output_list.append(k.eval(last_output))

outputs_list.append(k.eval(outputs))

assert len(new_states) == 0

assert_list_pairwise(last_output_list, shape=False)

assert_list_pairwise(outputs_list, shape=False)

开发者ID:hello-sea，项目名称:DeepLearning_Wavelet-LSTM，代码行数:39，

注：本文中的keras.backend.rnn方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。