code stringlengths 3 6.57k |
|---|
w_grad.get_shape() |
merge_with(w.get_shape() |
nn_ops.bias_add_grad(dicfo) |
b_grad.get_shape() |
merge_with(b.get_shape() |
ops.RegisterGradient("BlockLSTM") |
_BlockLSTMGrad(op, *grad) |
op.get_attr("use_peephole") |
LSTMBlockCell(LayerRNNCell) |
gates (see above) |
super(LSTMBlockCell, self) |
__init__(_reuse=reuse, name=name) |
base_layer.InputSpec(ndim=2) |
state_size(self) |
rnn_cell_impl.LSTMStateTuple(self._num_units, self._num_units) |
output_size(self) |
build(self, inputs_shape) |
str(inputs_shape) |
init_ops.constant_initializer(0.0) |
self.add_variable(self._names["wci"], [self._num_units]) |
self.add_variable(self._names["wcf"], [self._num_units]) |
self.add_variable(self._names["wco"], [self._num_units]) |
call(self, inputs, state) |
cell (LSTM) |
len(state) |
ValueError("Expecting state to be a tuple with length 2.") |
array_ops.zeros([self._num_units]) |
state
(_, cs, _, _, _, _, h) |
rnn_cell_impl.LSTMStateTuple(cs, h) |
LSTMBlockWrapper(base_layer.Layer) |
num_units(self) |
cell (output dimension) |
vector (tensor) |
call(self, inputs, initial_state=None, dtype=None, sequence_length=None) |
vector (tensor) |
isinstance(inputs, list) |
array_ops.stack(inputs) |
inputs.get_shape() |
with_rank(3) |
ValueError("Expecting inputs_shape[2] to be set: %s" % inputs_shape) |
array_ops.shape(inputs) |
array_ops.shape(inputs) |
ValueError("Either initial_state or dtype needs to be specified") |
array_ops.stack([batch_size, self.num_units]) |
len(initial_state) |
ops.convert_to_tensor(sequence_length) |
array_ops.sequence_mask(sequence_length, time_len, dtype=dtype) |
array_ops.expand_dims(mask, [-1]) |
array_ops.expand_dims(initial_cell_state, [0]) |
array_ops.expand_dims(initial_output, [0]) |
array_ops.unstack(outputs) |
rnn_cell_impl.LSTMStateTuple(final_cell_state, final_output) |
_gather_states(self, data, indices, batch_size) |
out(i, j) |
data(indices(i) |
math_ops.range(batch_size) |
array_ops.reshape(data, [-1, self.num_units]) |
LSTMBlockFusedCell(LSTMBlockWrapper) |
forget_bias (default: 1) |
gates (see above) |
super(LSTMBlockFusedCell, self) |
__init__(_reuse=reuse, name=name) |
base_layer.InputSpec(ndim=3) |
num_units(self) |
cell (output dimension) |
build(self, input_shape) |
init_ops.constant_initializer(0.0) |
self.add_variable("w_i_diag", [self._num_units]) |
self.add_variable("w_f_diag", [self._num_units]) |
self.add_variable("w_o_diag", [self._num_units]) |
vector (tensor) |
state (cs) |
Output (h) |
inputs.get_shape() |
with_rank(3) |
array_ops.shape(inputs) |
array_ops.zeros([self._num_units], dtype=dtype) |
math_ops.to_int64(time_len) |
math_ops.to_int64(math_ops.reduce_max(sequence_length) |
transform(i,o) |
len(line.strip() |
line.strip() |
split(None, 1) |
trans.split() |
t.startswith("<") |
ntrans.append(t.lower() |
print("{} {}".format(key, " ".join(ntrans) |
argparse.ArgumentParser(description='') |
parser.add_argument('infile', nargs='?', type=argparse.FileType('r', encoding='utf-8') |
codecs.getreader('utf-8') |
parser.add_argument('outfile', nargs='?', type=argparse.FileType('w', encoding='utf-8') |
codecs.getwriter('utf-8') |
parser.parse_args() |
transform(args.infile, args.outfile) |
string_to_float(val) |
float(val) |
compute_atom_contact(model, threshold, excluded=[]) |
len(chain) |
np.zeros((res_count,res_count) |
chain.get_residues() |
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