Python backend.zeros方法代码示例

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def build(self, input_shape):
    # Generate the nb_affine weights and biases
    num_deg = 2 * self.max_degree + (1 - self.min_degree)
    self.W_list = [
        self.add_weight(
            name='kernel',
            shape=(int(input_shape[0][-1]), self.out_channel),
            initializer='glorot_uniform',
            trainable=True) for k in range(num_deg)
    ]
    self.b_list = [
        self.add_weight(
            name='bias',
            shape=(self.out_channel,),
            initializer='zeros',
            trainable=True) for k in range(num_deg)
    ]
    self.built = True 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def __init__(self,
               activation_fn='relu',
               biases_initializer='zeros',
               weights_initializer=None,
               **kwargs):
    """
    Parameters
    ----------
    activation_fn: object
      the Tensorflow activation function to apply to the output
    biases_initializer: callable object
      the initializer for bias values.  This may be None, in which case the layer
      will not include biases.
    weights_initializer: callable object
      the initializer for weight values
    """
    super(Highway, self).__init__(**kwargs)
    self.activation_fn = activation_fn
    self.biases_initializer = biases_initializer
    self.weights_initializer = weights_initializer 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def _single_batch_trf(self, vol):
        # vol should be vol_shape + [nb_features]
        # self.trf should be vol_shape + [nb_features] + [ndims]

        vol_shape = vol.shape.as_list()
        nb_input_dims = vol_shape[-1]

        # this is inefficient...
        new_vols = [None] * self.output_features
        for j in range(self.output_features):
            new_vols[j] = tf.zeros(vol_shape[:-1], dtype=tf.float32)
            for i in range(nb_input_dims):
                trf_vol = transform(vol[..., i], self.trf[..., i, j, :] * self.trf_mult, interp_method=self.interp_method)
                trf_vol = tf.reshape(trf_vol, vol_shape[:-1])
                new_vols[j] += trf_vol * self.mult[..., i, j]

                if self.use_bias:
                    new_vols[j] += self.bias[..., j]
        
        return tf.stack(new_vols, -1) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def build(self, input_shape):
        # Create mean and count
        # These are weights because just maintaining variables don't get saved with the model, and we'd like
        # to have these numbers saved when we save the model.
        # But we need to make sure that the weights are untrainable.
        self.mean = self.add_weight(name='mean', 
                                      shape=input_shape[1:],
                                      initializer='zeros',
                                      trainable=False)
        self.count = self.add_weight(name='count', 
                                      shape=[1],
                                      initializer='zeros',
                                      trainable=False)

        # self.mean = K.zeros(input_shape[1:], name='mean')
        # self.count = K.variable(0.0, name='count')
        super(MeanStream, self).build(input_shape)  # Be sure to call this somewhere! 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def reset_spikevars(self, sample_idx):
        """
        Reset variables present in spiking layers. Can be turned off for
        instance when a video sequence is tested.
        """

        mod = self.config.getint('simulation', 'reset_between_nth_sample')
        mod = mod if mod else sample_idx + 1
        do_reset = sample_idx % mod == 0
        if do_reset:
            k.set_value(self.mem, self.init_membrane_potential())
        k.set_value(self.time, np.float32(self.dt))
        zeros_output_shape = np.zeros(self.output_shape, k.floatx())
        if self.tau_refrac > 0:
            k.set_value(self.refrac_until, zeros_output_shape)
        if self.spiketrain is not None:
            k.set_value(self.spiketrain, zeros_output_shape)
        k.set_value(self.last_spiketimes, zeros_output_shape - 1) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def init_neurons(self, input_shape):
        """Init layer neurons."""

        from snntoolbox.bin.utils import get_log_keys, get_plot_keys

        output_shape = self.compute_output_shape(input_shape)
        self.v_thresh = k.variable(self._v_thresh)
        self.mem = k.variable(self.init_membrane_potential(output_shape))
        self.time = k.variable(self.dt)
        # To save memory and computations, allocate only where needed:
        if self.tau_refrac > 0:
            self.refrac_until = k.zeros(output_shape)
        if any({'spiketrains', 'spikerates', 'correlation', 'spikecounts',
                'hist_spikerates_activations', 'operations',
                'synaptic_operations_b_t', 'neuron_operations_b_t',
                'spiketrains_n_b_l_t'} & (get_plot_keys(self.config) |
               get_log_keys(self.config))):
            self.spiketrain = k.zeros(output_shape)
        self.last_spiketimes = k.variable(-np.ones(output_shape)) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def reset_spikevars(self, sample_idx):
        """
        Reset variables present in spiking layers. Can be turned off for
        instance when a video sequence is tested.
        """

        mod = self.config.getint('simulation', 'reset_between_nth_sample')
        mod = mod if mod else sample_idx + 1
        do_reset = sample_idx % mod == 0
        if do_reset:
            k.set_value(self.mem, self.init_membrane_potential())
        k.set_value(self.time, np.float32(self.dt))
        zeros_output_shape = np.zeros(self.output_shape, k.floatx())
        if self.tau_refrac > 0:
            k.set_value(self.refrac_until, zeros_output_shape)
        if self.spiketrain is not None:
            k.set_value(self.spiketrain, zeros_output_shape)
        k.set_value(self.last_spiketimes, zeros_output_shape - 1)
        k.set_value(self.v_thresh, zeros_output_shape + self._v_thresh)
        k.set_value(self.prospective_spikes, zeros_output_shape)
        k.set_value(self.missing_impulse, zeros_output_shape) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def reset_spikevars(self, sample_idx):
        """
        Reset variables present in spiking layers. Can be turned off for
        instance when a video sequence is tested.
        """

        mod = self.config.getint('simulation', 'reset_between_nth_sample')
        mod = mod if mod else sample_idx + 1
        do_reset = sample_idx % mod == 0
        if do_reset:
            k.set_value(self.mem, self.init_membrane_potential())
        k.set_value(self.time, np.float32(self.dt))
        zeros_output_shape = np.zeros(self.output_shape, k.floatx())
        if self.spiketrain is not None:
            k.set_value(self.spiketrain, zeros_output_shape)
        k.set_value(self.last_spiketimes, zeros_output_shape - 1) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def rel_to_abs(self, x):
        shape = K.shape(x)
        shape = [shape[i] for i in range(3)]
        B, Nh, L, = shape
        col_pad = K.zeros(K.stack([B, Nh, L, 1]))
        x = K.concatenate([x, col_pad], axis=3)
        flat_x = K.reshape(x, [B, Nh, L * 2 * L])
        flat_pad = K.zeros(K.stack([B, Nh, L - 1]))
        flat_x_padded = K.concatenate([flat_x, flat_pad], axis=2)
        final_x = K.reshape(flat_x_padded, [B, Nh, L + 1, 2 * L - 1])
        final_x = final_x[:, :, :L, L - 1:]
        return final_x 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def call(self, inputs):
    atom_features = inputs[0]
    deg_slice = inputs[1]
    deg_adj_lists = inputs[3:]

    # Perform the mol gather
    # atom_features = graph_pool(atom_features, deg_adj_lists, deg_slice,
    #                            self.max_degree, self.min_degree)

    deg_maxed = (self.max_degree + 1 - self.min_degree) * [None]

    # Tensorflow correctly processes empty lists when using concat

    split_features = tf.split(atom_features, deg_slice[:, 1])
    for deg in range(1, self.max_degree + 1):
      # Get self atoms
      self_atoms = split_features[deg - self.min_degree]

      if deg_adj_lists[deg - 1].shape[0] == 0:
        # There are no neighbors of this degree, so just create an empty tensor directly.
        maxed_atoms = tf.zeros((0, self_atoms.shape[-1]))
      else:
        # Expand dims
        self_atoms = tf.expand_dims(self_atoms, 1)

        # always deg-1 for deg_adj_lists
        gathered_atoms = tf.gather(atom_features, deg_adj_lists[deg - 1])
        gathered_atoms = tf.concat(axis=1, values=[self_atoms, gathered_atoms])

        maxed_atoms = tf.reduce_max(gathered_atoms, 1)
      deg_maxed[deg - self.min_degree] = maxed_atoms

    if self.min_degree == 0:
      self_atoms = split_features[0]
      deg_maxed[0] = self_atoms

    return tf.concat(axis=0, values=deg_maxed) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def get_initial_states(self, input_shape):
    return [backend.zeros(input_shape), backend.zeros(input_shape)] 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def __init__(self, filters,
                 kernel_size,
                 strides=(1, 1, 1),
                 padding='valid',
                 data_format=None,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        
        super(LocallyConnected3D, self).__init__(**kwargs)
        self.filters = filters
        self.kernel_size = conv_utils.normalize_tuple(
            kernel_size, 3, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, 3, 'strides')
        self.padding = conv_utils.normalize_padding(padding)
        if self.padding != 'valid':
            raise ValueError('Invalid border mode for LocallyConnected3D '
                             '(only "valid" is supported): ' + padding)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.activation = activations.get(activation)
        self.use_bias = use_bias
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.input_spec = InputSpec(ndim=5) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def init_neurons(self, input_shape):
        """Init layer neurons."""

        from snntoolbox.bin.utils import get_log_keys, get_plot_keys

        output_shape = self.compute_output_shape(input_shape)
        self.mem = k.variable(self.init_membrane_potential(output_shape))
        self.time = k.variable(self.dt)
        if any({'spiketrains', 'spikerates', 'correlation', 'spikecounts',
                'hist_spikerates_activations', 'operations',
                'synaptic_operations_b_t', 'neuron_operations_b_t',
                'spiketrains_n_b_l_t'} & (get_plot_keys(self.config) |
               get_log_keys(self.config))):
            self.spiketrain = k.zeros(output_shape)
        self.last_spiketimes = k.variable(-np.ones(output_shape)) 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def init_membrane_potential(self, output_shape=None, mode='zero'):
        """Initialize membrane potential.

        Helpful to avoid transient response in the beginning of the simulation.
        Not needed when reset between frames is turned off, e.g. with a video
        data set.

        Parameters
        ----------

        output_shape: Optional[tuple]
            Output shape
        mode: str
            Initialization mode.

            - ``'uniform'``: Random numbers from uniform distribution in
              ``[-thr, thr]``.
            - ``'bias'``: Negative bias.
            - ``'zero'``: Zero (default).

        Returns
        -------

        init_mem: ndarray
            A tensor of ``self.output_shape`` (same as layer).
        """

        if output_shape is None:
            output_shape = self.output_shape

        if mode == 'uniform':
            init_mem = k.random_uniform(output_shape,
                                        -self._v_thresh, self._v_thresh)
        elif mode == 'bias':
            init_mem = np.zeros(output_shape, k.floatx())
            if hasattr(self, 'bias'):
                bias = self.get_weights()[1]
                for i in range(len(bias)):
                    # Todo: This assumes data_format = 'channels_first'
                    init_mem[:, i, Ellipsis] = bias[i]
                self.add_update([(self.bias, np.zeros_like(bias))])
        else:  # mode == 'zero':
            init_mem = np.zeros(output_shape, k.floatx())
        return init_mem 

# 需要导入模块: from tensorflow.keras import backend [as 别名]
# 或者: from tensorflow.keras.backend import zeros [as 别名]
def init_membrane_potential(self, output_shape=None, mode='zero'):
        """Initialize membrane potential.

        Helpful to avoid transient response in the beginning of the simulation.
        Not needed when reset between frames is turned off, e.g. with a video
        data set.

        Parameters
        ----------

        output_shape: Optional[tuple]
            Output shape
        mode: str
            Initialization mode.

            - ``'uniform'``: Random numbers from uniform distribution in
              ``[-thr, thr]``.
            - ``'bias'``: Negative bias.
            - ``'zero'``: Zero (default).

        Returns
        -------

        init_mem: ndarray
            A tensor of ``self.output_shape`` (same as layer).
        """

        if output_shape is None:
            output_shape = self.output_shape

        if mode == 'uniform':
            init_mem = k.random_uniform(output_shape,
                                        -self._v_thresh, self._v_thresh)
        elif mode == 'bias':
            init_mem = np.zeros(output_shape, k.floatx())
            if hasattr(self, 'b'):
                b = self.get_weights()[1]
                for i in range(len(b)):
                    init_mem[:, i, Ellipsis] = -b[i]
        else:  # mode == 'zero':
            init_mem = np.zeros(output_shape, k.floatx())
        return init_mem