Python backend.concatenate方法代碼示例

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def call(self, inputs):
        features = inputs[0]
        fltr = inputs[1]

        # Enforce sparse representation
        if not K.is_sparse(fltr):
            fltr = ops.dense_to_sparse(fltr)

        # Propagation
        indices = fltr.indices
        N = tf.shape(features, out_type=indices.dtype)[0]
        indices = ops.sparse_add_self_loops(indices, N)
        targets, sources = indices[:, -2], indices[:, -1]
        messages = tf.gather(features, sources)
        aggregated = self.aggregate_op(messages, targets, N)
        output = K.concatenate([features, aggregated])
        output = ops.dot(output, self.kernel)

        if self.use_bias:
            output = K.bias_add(output, self.bias)
        output = K.l2_normalize(output, axis=-1)
        if self.activation is not None:
            output = self.activation(output)
        return output 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def _rotation_matrix_zyz(self, params):
        phi = params[0] * 2 * np.pi - np.pi;       theta = params[1] * 2 * np.pi - np.pi;     psi_t = params[2] * 2 * np.pi - np.pi;
        
        loc_r = params[3:6] * 2 - 1
        
        
        a1 = self._rotation_matrix_axis(2, psi_t)       # first rotate about z axis for angle psi_t
        a2 = self._rotation_matrix_axis(1, theta)
        a3 = self._rotation_matrix_axis(2, phi)     
        rm = K.dot(K.dot(a3,a2),a1)
        
        rm = tf.transpose(rm)
        
        c = K.dot(-rm, K.expand_dims(loc_r))
        
        rm = K.flatten(rm)
        
        theta = K.concatenate([rm[:3], c[0], rm[3:6], c[1], rm[6:9], c[2]])

        return theta 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def _mask_rotation_matrix_zyz(self, params):
        phi = params[0] * 2 * np.pi - np.pi;       theta = params[1] * 2 * np.pi - np.pi;     psi_t = params[2] * 2 * np.pi - np.pi;
        
        loc_r = params[3:6] * 0    # magnitude of Fourier transformation is translation-invariant 
        
        
        a1 = self._rotation_matrix_axis(2, psi_t)
        a2 = self._rotation_matrix_axis(1, theta)
        a3 = self._rotation_matrix_axis(2, phi)     
        rm = K.dot(K.dot(a3,a2),a1)
        
        rm = tf.transpose(rm)
        
        c = K.dot(-rm, K.expand_dims(loc_r))
        
        rm = K.flatten(rm)
        
        theta = K.concatenate([rm[:3], c[0], rm[3:6], c[1], rm[6:9], c[2]])

        return theta 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def surv_likelihood(n_intervals):
  """Create custom Keras loss function for neural network survival model. 
  Arguments
      n_intervals: the number of survival time intervals
  Returns
      Custom loss function that can be used with Keras
  """
  def loss(y_true, y_pred):
    """
    Required to have only 2 arguments by Keras.
    Arguments
        y_true: Tensor.
          First half of the values is 1 if individual survived that interval, 0 if not.
          Second half of the values is for individuals who failed, and is 1 for time interval during which failure occured, 0 for other intervals.
          See make_surv_array function.
        y_pred: Tensor, predicted survival probability (1-hazard probability) for each time interval.
    Returns
        Vector of losses for this minibatch.
    """
    cens_uncens = 1. + y_true[:,0:n_intervals] * (y_pred-1.) #component for all individuals
    uncens = 1. - y_true[:,n_intervals:2*n_intervals] * y_pred #component for only uncensored individuals
    return K.sum(-K.log(K.clip(K.concatenate((cens_uncens,uncens)),K.epsilon(),None)),axis=-1) #return -log likelihood
  return loss 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def call(self, x, **kwargs):
        assert isinstance(x, list)
        inp_a, inp_b = x
        last_state = K.expand_dims(inp_b[:, -1, :], 1)
        m = []
        for i in range(self.output_dim):
            outp_a = inp_a * self.W[i]
            outp_last = last_state * self.W[i]
            outp_a = K.l2_normalize(outp_a, -1)
            outp_last = K.l2_normalize(outp_last, -1)
            outp = K.batch_dot(outp_a, outp_last, axes=[2, 2])
            m.append(outp)
        if self.output_dim > 1:
            persp = K.concatenate(m, 2)
        else:
            persp = m[0]
        return [persp, persp] 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def _find_maxima(x, coordinate_scale=1, confidence_scale=255.0):

    x = K.cast(x, K.floatx())

    col_max = K.max(x, axis=1)
    row_max = K.max(x, axis=2)

    maxima = K.max(col_max, 1)
    maxima = K.expand_dims(maxima, -2) / confidence_scale

    cols = K.cast(K.argmax(col_max, -2), K.floatx())
    rows = K.cast(K.argmax(row_max, -2), K.floatx())
    cols = K.expand_dims(cols, -2) * coordinate_scale
    rows = K.expand_dims(rows, -2) * coordinate_scale

    maxima = K.concatenate([cols, rows, maxima], -2)

    return maxima 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def call(self, x):
        output = self._spectrogram_mono(x[:, 0:1, :])
        if self.is_mono is False:
            for ch_idx in range(1, self.n_ch):
                output = K.concatenate(
                    (output, self._spectrogram_mono(x[:, ch_idx : ch_idx + 1, :])),
                    axis=self.ch_axis_idx,
                )
        if self.power_spectrogram != 2.0:
            output = K.pow(K.sqrt(output), self.power_spectrogram)
        if self.return_decibel_spectrogram:
            output = backend_keras.amplitude_to_decibel(output)
        return output 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
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 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def augmented_conv2d(ip, filters, kernel_size=(3, 3), strides=(1, 1),
                     depth_k=0.2, depth_v=0.2, num_heads=8, relative_encodings=True):
    """
    Builds an Attention Augmented Convolution block.

    Args:
        ip: keras tensor.
        filters: number of output filters.
        kernel_size: convolution kernel size.
        strides: strides of the convolution.
        depth_k: float or int. Number of filters for k.
            Computes the number of filters for `v`.
            If passed as float, computed as `filters * depth_k`.
        depth_v: float or int. Number of filters for v.
            Computes the number of filters for `k`.
            If passed as float, computed as `filters * depth_v`.
        num_heads: int. Number of attention heads.
            Must be set such that `depth_k // num_heads` is > 0.
        relative_encodings: bool. Whether to use relative
            encodings or not.

    Returns:
        a keras tensor.
    """
    # input_shape = K.int_shape(ip)
    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1

    depth_k, depth_v = _normalize_depth_vars(depth_k, depth_v, filters)

    conv_out = _conv_layer(filters - depth_v, kernel_size, strides)(ip)

    # Augmented Attention Block
    qkv_conv = _conv_layer(2 * depth_k + depth_v, (1, 1), strides)(ip)
    attn_out = AttentionAugmentation2D(depth_k, depth_v, num_heads, relative_encodings)(qkv_conv)
    attn_out = _conv_layer(depth_v, kernel_size=(1, 1))(attn_out)

    output = concatenate([conv_out, attn_out], axis=channel_axis)
    output = BatchNormalization()(output)
    return output 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def call(self, inputs):
    """Execute this layer on input tensors.

    Parameters
    ----------
    inputs: list
      List of two tensors (X, Xp). X should be of shape (n_test,
      n_feat) and Xp should be of shape (n_support, n_feat) where
      n_test is the size of the test set, n_support that of the support
      set, and n_feat is the number of per-atom features.

    Returns
    -------
    list
      Returns two tensors of same shape as input. Namely the output
      shape will be [(n_test, n_feat), (n_support, n_feat)]
    """
    if len(inputs) != 2:
      raise ValueError("AttnLSTMEmbedding layer must have exactly two parents")
    # x is test set, xp is support set.
    x, xp = inputs

    # Get initializations
    q = self.q_init
    states = self.states_init

    for d in range(self.max_depth):
      # Process using attention
      # Eqn (4), appendix A.1 of Matching Networks paper
      e = _cosine_dist(x + q, xp)
      a = tf.nn.softmax(e)
      r = backend.dot(a, xp)

      # Generate new attention states
      y = backend.concatenate([q, r], axis=1)
      q, states = self.lstm([y] + states)
    return [x + q, xp] 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def build(self, input_shape):
    init = initializers.get(self.init)
    self.U = init((2 * self.n_hidden, 4 * self.n_hidden))
    self.b = tf.Variable(
        np.concatenate((np.zeros(self.n_hidden), np.ones(self.n_hidden),
                        np.zeros(self.n_hidden), np.zeros(self.n_hidden))),
        dtype=tf.float32)
    self.built = True 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def message(self, X, **kwargs):
        X_i = self.get_i(X)
        X_j = self.get_j(X)
        return self.mlp(K.concatenate((X_i, X_j - X_i))) 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def call(self, inputs, **kwargs):
        X, A, E = self.get_inputs(inputs)
        edge_weight = A.values

        output = [X]
        for k in range(self.K):
            output.append(self.propagate(X, A, E, edge_weight=edge_weight))
        output = K.concatenate(output)

        return self.linear(output) 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def message(self, X, E=None):
        X_i = self.get_i(X)
        X_j = self.get_j(X)
        Z = K.concatenate((X_i, X_j, E), axis=-1)
        output = self.dense_s(Z) * self.dense_f(Z)

        return output 

# 需要導入模塊: from tensorflow.keras import backend [as 別名]
# 或者: from tensorflow.keras.backend import concatenate [as 別名]
def _concat(lists, dim):
    if lists[0].size == 0:
        lists = lists[1:]

    return np.concatenate(lists, dim)