a214f4e64d9e1f09a6ffdcad5e9595d0534e08f0,keras/layers/recurrent.py,LSTM,build,#LSTM#Any#,880

Before Change

        self.recurrent_dropout = min(1., max(0., recurrent_dropout))

    def build(self, input_shape):
        self.input_spec = InputSpec(shape=input_shape)
        self.input_dim = input_shape[2]

        if self.stateful:
            self.reset_states()
        else:
            // initial states: 2 all-zero tensors of shape (output_dim)
            self.states = [None, None]

        self.kernel = self.add_weight((self.input_dim, self.units * 4),
                                      name="kernel",
                                      initializer=self.kernel_initializer,
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        self.recurrent_kernel = self.add_weight(
            (self.units, self.units * 4),
            name="recurrent_kernel",
            initializer=self.recurrent_initializer,
            regularizer=self.recurrent_regularizer,
            constraint=self.recurrent_constraint)

        if self.use_bias:
            self.bias = self.add_weight((self.units * 4,),
                                        name="bias",
                                        initializer=self.bias_initializer,
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
            if self.unit_forget_bias:
                self.bias += K.concatenate([K.zeros((self.units,)),
                                           K.ones((self.units,)),
                                           K.zeros((self.units * 2,))])
        else:
            self.bias = None

        self.kernel_i = self.kernel[:, :self.units]
        self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units]
        self.kernel_f = self.kernel[:, self.units: self.units * 2]
        self.recurrent_kernel_f = self.recurrent_kernel[:, self.units: self.units * 2]
        self.kernel_c = self.kernel[:, self.units * 2: self.units * 3]
        self.recurrent_kernel_c = self.recurrent_kernel[:, self.units * 2: self.units * 3]
        self.kernel_o = self.kernel[:, self.units * 3:]
        self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:]

        if self.use_bias:

After Change

        self.recurrent_dropout = min(1., max(0., recurrent_dropout))

    def build(self, input_shape):
        if isinstance(input_shape, list):
            input_shape = input_shape[0]
        self.input_dim = input_shape[2]

        if self.stateful:
            self.reset_states()
        else:
            // initial states: 2 all-zero tensors of shape (output_dim)
            self.states = [None, None]

        self.kernel = self.add_weight((self.input_dim, self.units * 4),
                                      name="kernel",
                                      initializer=self.kernel_initializer,
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        self.recurrent_kernel = self.add_weight(
            (self.units, self.units * 4),
            name="recurrent_kernel",
            initializer=self.recurrent_initializer,
            regularizer=self.recurrent_regularizer,
            constraint=self.recurrent_constraint)

        if self.use_bias:
            self.bias = self.add_weight((self.units * 4,),
                                        name="bias",
                                        initializer=self.bias_initializer,
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
            if self.unit_forget_bias:
                self.bias += K.concatenate([K.zeros((self.units,)),
                                           K.ones((self.units,)),
                                           K.zeros((self.units * 2,))])
        else:
            self.bias = None

        self.kernel_i = self.kernel[:, :self.units]
        self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units]
        self.kernel_f = self.kernel[:, self.units: self.units * 2]
        self.recurrent_kernel_f = self.recurrent_kernel[:, self.units: self.units * 2]
        self.kernel_c = self.kernel[:, self.units * 2: self.units * 3]
        self.recurrent_kernel_c = self.recurrent_kernel[:, self.units * 2: self.units * 3]
        self.kernel_o = self.kernel[:, self.units * 3:]
        self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:]

        if self.use_bias:

Instances