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@ -262,9 +262,10 @@ def embedding(input,
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return tmp
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# TODO(qijun): expose H0 and C0
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def dynamic_lstm(input,
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size,
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h_0=None,
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c_0=None,
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param_attr=None,
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bias_attr=None,
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use_peepholes=True,
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@ -325,6 +326,13 @@ def dynamic_lstm(input,
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(T X 4D), where T is the total time steps in this
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mini-batch, D is the hidden size.
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size(int): 4 * hidden size.
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h_0(Variable): The initial hidden state is an optional input, default is zero.
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This is a tensor with shape (N x D), where N is the
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batch size and D is the hidden size.
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c_0(Variable): The initial cell state is an optional input, default is zero.
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This is a tensor with shape (N x D), where N is the
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batch size. `h_0` and `c_0` can be NULL but only at the same time.
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param_attr(ParamAttr|None): The parameter attribute for the learnable
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hidden-hidden weights.
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@ -388,12 +396,20 @@ def dynamic_lstm(input,
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cell = helper.create_tmp_variable(dtype)
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batch_gate = helper.create_tmp_variable(dtype)
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batch_cell_pre_act = helper.create_tmp_variable(dtype)
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inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
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batch_size = input.shape[0]
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if h_0:
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assert h_0.shape == (batch_size, size), \
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'The shape of h0 should be (batch_size, %d)' % size
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inputs['H0'] = h_0
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if c_0:
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assert c_0.shape == (batch_size, size), \
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'The shape of c0 should be (batch_size, %d)' % size
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inputs['C0'] = c_0
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helper.append_op(
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type='lstm',
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inputs={'Input': input,
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'Weight': weight,
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'Bias': bias},
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inputs=inputs,
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outputs={
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'Hidden': hidden,
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'Cell': cell,
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@ -678,11 +694,13 @@ def dynamic_gru(input,
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attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
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bias = helper.create_parameter(
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attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
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batch_size = input.shape[0]
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inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
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if h_0 != None:
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assert h_0.shape == (
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size, size), 'The shape of h0 should be(%d, %d)' % (size, size)
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inputs['h0'] = h_0
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batch_size, size
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), 'The shape of h0 should be(batch_size, %d)' % size
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inputs['H0'] = h_0
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hidden = helper.create_tmp_variable(dtype)
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batch_gate = helper.create_tmp_variable(dtype)
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fengjiayi
7 years ago