tf.contrib.rnn.GRUBlockCell.output_size

class tf.contrib.rnn.GRUBlockCell Block GRU cell implementation. The implementation is based on: http://arxiv.org/abs/1406.1078 Computes the LSTM cell forward propagation for 1 time step. This kernel op implements the following mathematical equations: Baises are initialized with : b_ru - constant_initializer(1.0) b_c - constant_initializer(0.0) ``` x_h_prev = [x, h_prev] [r_bar u_bar] = x_h_prev * w_ru + b_ru r = sigmoid(r_bar) u = sigmoid(u_bar) h_prevr = h_prev \circ r x_h_prevr = [x h_prevr

tf.contrib.rnn.GridLSTMCell.__init__(num_units, use_peepholes=False, share_time_frequency_weights=False, cell_clip=None, initializer=None, num_unit_shards=1, forget_bias=1.0, feature_size=None, frequency_skip=None, num_frequency_blocks=1, couple_input_forget_gates=False, state_is_tuple=False) Initialize the parameters for an LSTM cell. Args: num_units: int, The number of units in the LSTM cell use_peepholes: bool, default False. Set True to enable diagonal/peephole connections. share_time_f

tf.contrib.rnn.GridLSTMCell.__call__(inputs, state, scope=None) Run one step of LSTM. Args: inputs: input Tensor, 2D, batch x num_units. state: state Tensor, 2D, batch x state_size. scope: VariableScope for the created subgraph; defaults to "LSTMCell". Returns: A tuple containing: - A 2D, batch x output_dim, Tensor representing the output of the LSTM after reading "inputs" when previous state was "state". Here output_dim is num_units. - A 2D, batch x state_size, Tensor representing the ne

tf.contrib.rnn.GridLSTMCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shape

tf.contrib.rnn.GridLSTMCell.state_tuple_type

tf.contrib.rnn.GridLSTMCell.state_size

tf.contrib.rnn.GridLSTMCell.output_size

class tf.contrib.rnn.GridLSTMCell Grid Long short-term memory unit (LSTM) recurrent network cell. The default is based on: Nal Kalchbrenner, Ivo Danihelka and Alex Graves "Grid Long Short-Term Memory," Proc. ICLR 2016. http://arxiv.org/abs/1507.01526 When peephole connections are used, the implementation is based on: Tara N. Sainath and Bo Li "Modeling Time-Frequency Patterns with LSTM vs. Convolutional Architectures for LVCSR Tasks." submitted to INTERSPEECH, 2016. The code uses optional peep

tf.contrib.rnn.CoupledInputForgetGateLSTMCell.__init__(num_units, use_peepholes=False, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=False, activation=tanh) Initialize the parameters for an LSTM cell. Args: num_units: int, The number of units in the LSTM cell use_peepholes: bool, set True to enable diagonal/peephole connections. initializer: (optional) The initializer to use for the weight and projection matrices. num