我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用tensorflow.python.framework.tensor_shape.matrix()。
def _reverse_seq(input_seq, lengths): """Reverse a list of Tensors up to specified lengths. Args: input_seq: Sequence of seq_len tensors of dimension (batch_size, depth) lengths: A tensor of dimension batch_size, containing lengths for each sequence in the batch. If "None" is specified, simply reverses the list. Returns: time-reversed sequence """ if lengths is None: return list(reversed(input_seq)) input_shape = tensor_shape.matrix(None, None) for input_ in input_seq: input_shape.merge_with(input_.get_shape()) input_.set_shape(input_shape) # Join into (time, batch_size, depth) s_joined = array_ops.pack(input_seq) # TODO(schuster, ebrevdo): Remove cast when reverse_sequence takes int32 if lengths is not None: lengths = math_ops.to_int64(lengths) # Reverse along dimension 0 s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1) # Split again into list result = array_ops.unpack(s_reversed) for r in result: r.set_shape(input_shape) return result
def _reverse_seq(input_seq, lengths): """Reverse a list of Tensors up to specified lengths. Args: input_seq: Sequence of seq_len tensors of dimension (batch_size, depth) lengths: A tensor of dimension batch_size, containing lengths for each sequence in the batch. If "None" is specified, simply reverses the list. Returns: time-reversed sequence """ if lengths is None: return list(reversed(input_seq)) input_shape = tensor_shape.matrix(None, None) for input_ in input_seq: input_shape.merge_with(input_.get_shape()) input_.set_shape(input_shape) # Join into (time, batch_size, depth) s_joined = tf.stack(input_seq) if lengths is not None: lengths = tf.to_int64(lengths) # Reverse along dimension 0 s_reversed = tf.reverse_sequence(s_joined, lengths, 0, 1) # Split again into list result = tf.unstack(s_reversed) for r in result: r.set_shape(input_shape) return result
def linear(args, output_size, bias, bias_start=0.0, scope=None): """Linear map: sum_i(args[i] * W[i]), where W[i] is a variable. Args: args: a 2D Tensor or a list of 2D, batch x n, Tensors. output_size: int, second dimension of W[i]. bias: boolean, whether to add a bias term or not. bias_start: starting value to initialize the bias; 0 by default. scope: VariableScope for the created subgraph; defaults to "Linear". Returns: A 2D Tensor with shape [batch x output_size] equal to sum_i(args[i] * W[i]), where W[i]s are newly created matrices. Raises: ValueError: if some of the arguments has unspecified or wrong shape. """ if args is None or (isinstance(args, (list, tuple)) and not args): raise ValueError('`args` must be specified') if not isinstance(args, (list, tuple)): args = [args] # Calculate the total size of arguments on dimension 1. total_arg_size = 0 shapes = [a.get_shape().as_list() for a in args] for shape in shapes: if len(shape) != 2: raise ValueError('Linear is expecting 2D arguments: %s' % str(shapes)) if not shape[1]: raise ValueError('Linear expects shape[1] of arguments: %s' % str(shapes)) else: total_arg_size += shape[1] # Now the computation. with tf.variable_scope(scope or 'Linear'): matrix = tf.get_variable('Matrix', [total_arg_size, output_size]) if len(args) == 1: res = tf.matmul(args[0], matrix) else: res = tf.matmul(tf.concat(args, 1), matrix) if not bias: return res bias_term = tf.get_variable( 'Bias', [output_size], initializer=tf.constant_initializer(bias_start)) return res + bias_term
