Python backend.conv1d方法代码示例

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import conv1d [as 别名]
def call(self, u_vecs):
        if self.share_weights:
            u_hat_vecs = K.conv1d(u_vecs, self.W)
        else:
            u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])

        batch_size = K.shape(u_vecs)[0]
        input_num_capsule = K.shape(u_vecs)[1]
        u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule,
                                            self.num_capsule, self.dim_capsule))
        u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1, 3))

        b = K.zeros_like(u_hat_vecs[:, :, :, 0])  # shape = [None, num_capsule, input_num_capsule]
        for i in range(self.routings):
            b = K.permute_dimensions(b, (0, 2, 1))  # shape = [None, input_num_capsule, num_capsule]
            c = K.softmax(b)
            c = K.permute_dimensions(c, (0, 2, 1))
            b = K.permute_dimensions(b, (0, 2, 1))
            outputs = self.activation(K.batch_dot(c, u_hat_vecs, [2, 2]))
            if i < self.routings - 1:
                b = K.batch_dot(outputs, u_hat_vecs, [2, 3])

        return outputs 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import conv1d [as 别名]
def call(self, u_vecs):
        if self.share_weights:
            u_hat_vecs = K.conv1d(u_vecs, self.W)
        else:
            u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])

        batch_size = K.shape(u_vecs)[0]
        input_num_capsule = K.shape(u_vecs)[1]
        u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule,
                                            self.num_capsule, self.dim_capsule))
        u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1, 3))
        # final u_hat_vecs.shape = [None, num_capsule, input_num_capsule, dim_capsule]

        b = K.zeros_like(u_hat_vecs[:, :, :, 0])  # shape = [None, num_capsule, input_num_capsule]
        outputs = None
        for i in range(self.routings):
            b = K.permute_dimensions(b, (0, 2, 1))  # shape = [None, input_num_capsule, num_capsule]
            c = K.softmax(b)
            c = K.permute_dimensions(c, (0, 2, 1))
            b = K.permute_dimensions(b, (0, 2, 1))
            outputs = self.activation(K.batch_dot(c, u_hat_vecs, [2, 2]))
            if i < self.routings - 1:
                b = K.batch_dot(outputs, u_hat_vecs, [2, 3])

        return outputs 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import conv1d [as 别名]
def call(self, u_vecs):
        if self.share_weights:
            u_hat_vecs = K.conv1d(u_vecs, self.W)
        else:
            u_hat_vecs = K.local_conv1d(u_vecs, self.W, [1], [1])

        batch_size = K.shape(u_vecs)[0]
        input_num_capsule = K.shape(u_vecs)[1]
        u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule,
                                            self.num_capsule, self.dim_capsule))    # noqa
        u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1, 3))
        # final u_hat_vecs.shape = [None, num_capsule, input_num_capsule, dim_capsule]  # noqa

        b = K.zeros_like(u_hat_vecs[:, :, :, 0])  # shape = [None, num_capsule, input_num_capsule]  # noqa
        for i in range(self.routings):
            b = K.permute_dimensions(b, (0, 2, 1))  # shape = [None, input_num_capsule, num_capsule]    # noqa
            c = K.softmax(b)
            c = K.permute_dimensions(c, (0, 2, 1))
            b = K.permute_dimensions(b, (0, 2, 1))
            outputs = self.activation(tf.keras.backend.batch_dot(c, u_hat_vecs, [2, 2]))    # noqa
            if i < self.routings - 1:
                b = tf.keras.backend.batch_dot(outputs, u_hat_vecs, [2, 3])
        return outputs 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import conv1d [as 别名]
def call(self, inputs):
        if self.share_weights:
            u_hat_vectors = K.conv1d(inputs, self.W)
        else:
            u_hat_vectors = K.local_conv1d(inputs, self.W, [1], [1])

        # u_hat_vectors : The spatially transformed input vectors (with local_conv_1d)

        batch_size = K.shape(inputs)[0]
        input_num_capsule = K.shape(inputs)[1]
        u_hat_vectors = K.reshape(u_hat_vectors, (batch_size,
                                                  input_num_capsule,
                                                  self.num_capsule,
                                                  self.dim_capsule))

        u_hat_vectors = K.permute_dimensions(u_hat_vectors, (0, 2, 1, 3))
        routing_weights = K.zeros_like(u_hat_vectors[:, :, :, 0])

        for i in range(self.routings):
            capsule_weights = K.softmax(routing_weights, 1)
            outputs = K.batch_dot(capsule_weights, u_hat_vectors, [2, 2])
            if K.ndim(outputs) == 4:
                outputs = K.sum(outputs, axis=1)
            if i < self.routings - 1:
                outputs = K.l2_normalize(outputs, -1)
                routing_weights = K.batch_dot(outputs, u_hat_vectors, [2, 3])
                if K.ndim(routing_weights) == 4:
                    routing_weights = K.sum(routing_weights, axis=1)

        return self.activation(outputs) 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import conv1d [as 别名]
def call(self, inputs):
        input_shape = K.shape(inputs)
        if self.data_format == 'channels_first':
            channel_axis = 1
        else:
            channel_axis = -1
        if input_shape[channel_axis] is None:
            raise ValueError('The channel dimension of the inputs '
                             'should be defined. Found `None`.')
        input_dim = input_shape[channel_axis]
        ker_shape = self.kernel_size + (input_dim, self.filters)
        nb_kernels = ker_shape[-2] * ker_shape[-1]
        kernel_shape_4_norm = (np.prod(self.kernel_size), nb_kernels)
        reshaped_kernel = K.reshape(self.kernel, kernel_shape_4_norm)
        normalized_weight = K.l2_normalize(reshaped_kernel, axis=0, epsilon=self.epsilon)
        normalized_weight = K.reshape(self.gamma, (1, ker_shape[-2] * ker_shape[-1])) * normalized_weight
        shaped_kernel = K.reshape(normalized_weight, ker_shape)
        shaped_kernel._keras_shape = ker_shape
        
        convArgs = {"strides":       self.strides[0]       if self.rank == 1 else self.strides,
                    "padding":       self.padding,
                    "data_format":   self.data_format,
                    "dilation_rate": self.dilation_rate[0] if self.rank == 1 else self.dilation_rate}
        convFunc = {1: K.conv1d,
                    2: K.conv2d,
                    3: K.conv3d}[self.rank]
        output = convFunc(inputs, shaped_kernel, **convArgs)

        if self.use_bias:
            output = K.bias_add(
                output,
                self.bias,
                data_format=self.data_format
            )

        if self.activation is not None:
            output = self.activation(output)

        return output 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import conv1d [as 别名]
def call(self, inputs, training=None):
        def _l2normalize(v, eps=1e-12):
            return v / (K.sum(v ** 2) ** 0.5 + eps)
        def power_iteration(W, u):
            #Accroding the paper, we only need to do power iteration one time.
            _u = u
            _v = _l2normalize(K.dot(_u, K.transpose(W)))
            _u = _l2normalize(K.dot(_v, W))
            return _u, _v
        #Spectral Normalization
        W_shape = self.kernel.shape.as_list()
        #Flatten the Tensor
        W_reshaped = K.reshape(self.kernel, [-1, W_shape[-1]])
        _u, _v = power_iteration(W_reshaped, self.u)
        #Calculate Sigma
        sigma=K.dot(_v, W_reshaped)
        sigma=K.dot(sigma, K.transpose(_u))
        #normalize it
        W_bar = W_reshaped / sigma
        #reshape weight tensor
        if training in {0, False}:
            W_bar = K.reshape(W_bar, W_shape)
        else:
            with tf.control_dependencies([self.u.assign(_u)]):
                W_bar = K.reshape(W_bar, W_shape)
                
        outputs = K.conv1d(
                inputs,
                W_bar,
                strides=self.strides,
                padding=self.padding,
                data_format=self.data_format,
                dilation_rate=self.dilation_rate)
        if self.use_bias:
            outputs = K.bias_add(
                outputs,
                self.bias,
                data_format=self.data_format)
        if self.activation is not None:
            return self.activation(outputs)
        return outputs 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import conv1d [as 别名]
def call(self, inputs):
        """Following the routing algorithm from Hinton's paper,
        but replace b = b + <u,v> with b = <u,v>.

        This change can improve the feature representation of Capsule.

        However, you can replace
            b = K.batch_dot(outputs, hat_inputs, [2, 3])
        with
            b += K.batch_dot(outputs, hat_inputs, [2, 3])
        to realize a standard routing.
        """

        if self.share_weights:
            hat_inputs = K.conv1d(inputs, self.kernel)
        else:
            hat_inputs = K.local_conv1d(inputs, self.kernel, [1], [1])

        batch_size = K.shape(inputs)[0]
        input_num_capsule = K.shape(inputs)[1]
        hat_inputs = K.reshape(hat_inputs,
                               (batch_size, input_num_capsule,
                                self.num_capsule, self.dim_capsule))
        hat_inputs = K.permute_dimensions(hat_inputs, (0, 2, 1, 3))

        b = K.zeros_like(hat_inputs[:, :, :, 0])
        for i in range(self.routings):
            c = softmax(b, 1)
            if K.backend() == 'theano':
                o = K.sum(o, axis=1)
            o = self.activation(K.batch_dot(c, hat_inputs, [2, 2]))
            if i < self.routings - 1:
                b = K.batch_dot(o, hat_inputs, [2, 3])
                if K.backend() == 'theano':
                    o = K.sum(o, axis=1)

        return o 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import conv1d [as 别名]
def call(self, inputs):
        channel_axis = 1 if self.data_format == 'channels_first' else -1
        input_dim    = K.shape(inputs)[channel_axis] // 4
        index2 = self.filters*2
        index3 = self.filters*3
        if self.rank == 1:
            f_r   = self.kernel[:, :, :self.filters]
            f_i   = self.kernel[:, :, self.filters:index2]
            f_j   = self.kernel[:, :, index2:index3]
            f_k   = self.kernel[:, :, index3:]
        elif self.rank == 2:
            f_r   = self.kernel[:, :, :, :self.filters]
            f_i   = self.kernel[:, :, :, self.filters:index2]
            f_j   = self.kernel[:, :, :, index2:index3]
            f_k   = self.kernel[:, :, :, index3:]
        elif self.rank == 3:
            f_r   = self.kernel[:, :, :, :, :self.filters]
            f_i   = self.kernel[:, :, :, :, self.filters:index2]
            f_j   = self.kernel[:, :, :, :, index2:index3]
            f_k   = self.kernel[:, :, :, :, index3:]

        convArgs = {"strides":       self.strides[0]       if self.rank == 1 else self.strides,
                    "padding":       self.padding,
                    "data_format":   self.data_format,
                    "dilation_rate": self.dilation_rate[0] if self.rank == 1 else self.dilation_rate}
        convFunc = {1: K.conv1d,
                    2: K.conv2d,
                    3: K.conv3d}[self.rank]

                
        #
        # Performing quaternion convolution
        #
        
        f_r._keras_shape = self.kernel_shape
        f_i._keras_shape = self.kernel_shape
        f_j._keras_shape = self.kernel_shape
        f_k._keras_shape = self.kernel_shape

        cat_kernels_4_r = K.concatenate([f_r, -f_i, -f_j, -f_k], axis=-2)
        cat_kernels_4_i = K.concatenate([f_i, f_r, -f_k, f_j], axis=-2)
        cat_kernels_4_j = K.concatenate([f_j, f_k, f_r, -f_i], axis=-2)
        cat_kernels_4_k = K.concatenate([f_k, -f_j, f_i, f_r], axis=-2)
        cat_kernels_4_quaternion = K.concatenate([cat_kernels_4_r, cat_kernels_4_i, cat_kernels_4_j, cat_kernels_4_k], axis=-1)
        cat_kernels_4_quaternion._keras_shape = self.kernel_size + (4 * input_dim, 4 * self.filters)

        output = convFunc(inputs, cat_kernels_4_quaternion, **convArgs)

        if self.use_bias:
            output = K.bias_add(
                output,
                self.bias,
                data_format=self.data_format
            )
        if self.activation is not None:
            output = self.activation(output)

        return output 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import conv1d [as 别名]
def call(self, inputs, mask=None):
        # compute channels_firste candidate hidden state
         # Arguments
        '''
        x: Tensor or variable.
        kernel: kernel tensor.
        strides: strides tuple.
        padding: string, `"same"` or `"valid"`.
        data_format: string, `"channels_last"` or `"channels_first"`.
            Whether to use Theano or TensorFlow/CNTK data format
            for inputs/kernels/outputs.
        dilation_rate: tuple of 3 integers.
        '''
        transform = K.conv1d(
                inputs,
                self.kernel,
                strides=self.strides[0],
                padding=self.padding,
                data_format=self.data_format,
                dilation_rate=self.dilation_rate[0])

        if self.use_bias:
            transform = K.bias_add(
                transform,
                self.bias,
                data_format=self.data_format)

        if self.activation is not None:
            transform = self.activation(transform)

        transform_gate = K.conv1d(
                inputs,
                self.kernel_gate,
                strides=self.strides[0],
                padding=self.padding,
                data_format=self.data_format,
                dilation_rate=self.dilation_rate[0])

        if self.use_bias:
            transform = K.bias_add(
                transform,
                self.bias_gate,
                data_format=self.data_format)
        
        transform_gate = K.sigmoid(transform_gate)
       
        carry_gate = 1.0 - transform_gate

        return transform * transform_gate + inputs * carry_gate 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import conv1d [as 别名]
def call(self, inputs):
        if self.rank == 1:
            expert_outputs = K.conv1d(
                inputs,
                self.expert_kernel,
                strides=self.strides[0],
                padding=self.padding,
                data_format=self.data_format,
                dilation_rate=self.dilation_rate[0])
        if self.rank == 2:
            expert_outputs = K.conv2d(
                inputs,
                self.expert_kernel,
                strides=self.strides,
                padding=self.padding,
                data_format=self.data_format,
                dilation_rate=self.dilation_rate)
        if self.rank == 3:
            expert_outputs = K.conv3d(
                inputs,
                self.expert_kernel,
                strides=self.strides,
                padding=self.padding,
                data_format=self.data_format,
                dilation_rate=self.dilation_rate)

        expert_outputs = K.reshape(expert_outputs, (-1,) + self.o_shape[1:-1] + (self.n_filters, self.n_experts_per_filter))

        if self.use_expert_bias:
            expert_outputs = K.bias_add(
                expert_outputs,
                self.expert_bias,
                data_format=self.data_format)

        if self.expert_activation is not None:
            expert_outputs = self.expert_activation(expert_outputs)

        gating_outputs = tf.tensordot(inputs, self.gating_kernel, axes=self.rank+1) # samples x n_filters x n_experts_per_filter

        if self.use_gating_bias:
            gating_outputs = K.bias_add(
                gating_outputs,
                self.gating_bias,
                data_format=self.data_format)

        if self.gating_activation is not None:
            gating_outputs = self.gating_activation(gating_outputs)

        gating_outputs = K.reshape(gating_outputs, self.new_gating_outputs_shape)
        outputs = K.sum(expert_outputs * gating_outputs, axis=-1, keepdims=False)

        return outputs