uaca /
deepy
| Total Complexity | 40 |
| Total Lines | 150 |
| Duplicated Lines | 0 % |
Complex classes like deepy.layers.RNN often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | #!/usr/bin/env python |
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| 15 | class RNN(NeuralLayer): |
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| 16 | """ |
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| 17 | Recurrent neural network layer. |
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| 18 | """ |
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| 19 | |||
| 20 | def __init__(self, hidden_size, input_type="sequence", output_type="sequence", vector_core=None, |
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| 21 | hidden_activation="tanh", hidden_init=None, input_init=None, steps=None, |
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| 22 | persistent_state=False, reset_state_for_input=None, batch_size=None, |
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| 23 | go_backwards=False, mask=None, second_input_size=None, second_input=None): |
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| 24 | super(RNN, self).__init__("rnn") |
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| 25 | self._hidden_size = hidden_size |
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| 26 | self.output_dim = self._hidden_size |
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| 27 | self._input_type = input_type |
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| 28 | self._output_type = output_type |
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| 29 | self._hidden_activation = hidden_activation |
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| 30 | self._hidden_init = hidden_init |
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| 31 | self._vector_core = vector_core |
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| 32 | self._input_init = input_init |
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| 33 | self.persistent_state = persistent_state |
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| 34 | self.reset_state_for_input = reset_state_for_input |
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| 35 | self.batch_size = batch_size |
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| 36 | self._steps = steps |
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| 37 | self._go_backwards = go_backwards |
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| 38 | # mask |
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| 39 | mask = mask.tensor if type(mask) == NeuralVar else mask |
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| 40 | self._mask = mask.dimshuffle((1,0)) if mask else None |
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| 41 | # second input |
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| 42 | if type(second_input) == NeuralVar: |
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| 43 | second_input = second_input.tensor |
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| 44 | second_input_size = second_input.dim() |
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| 45 | self._second_input_size = second_input_size |
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| 46 | self._second_input = second_input |
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| 47 | self._sequence_map = OrderedDict() |
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| 48 | if input_type not in INPUT_TYPES: |
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| 49 | raise Exception("Input type of RNN is wrong: %s" % input_type) |
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| 50 | if output_type not in OUTPUT_TYPES: |
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| 51 | raise Exception("Output type of RNN is wrong: %s" % output_type) |
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| 52 | if self.persistent_state and not self.batch_size: |
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| 53 | raise Exception("Batch size must be set for persistent state mode") |
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| 54 | if mask and input_type == "one": |
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| 55 | raise Exception("Mask only works with sequence input") |
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| 56 | |||
| 57 | def _hidden_preact(self, h): |
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| 58 | return T.dot(h, self.W_h) if not self._vector_core else h * self.W_h |
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| 59 | |||
| 60 | def step(self, *vars): |
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| 61 | # Parse sequence |
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| 62 | sequence_map = dict(zip(self._sequence_map.keys(), vars[:len(self._sequence_map)])) |
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| 63 | if self._input_type == "sequence": |
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| 64 | x = sequence_map["x"] |
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| 65 | h = vars[-1] |
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| 66 | # Reset part of the state on condition |
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| 67 | if self.reset_state_for_input != None: |
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| 68 | h = h * T.neq(x[:, self.reset_state_for_input], 1).dimshuffle(0, 'x') |
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| 69 | # RNN core step |
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| 70 | z = x + self._hidden_preact(h) + self.B_h |
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| 71 | else: |
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| 72 | h = vars[-1] |
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| 73 | z = self._hidden_preact(h) + self.B_h |
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| 74 | # Second input |
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| 75 | if "second_input" in sequence_map: |
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| 76 | z += sequence_map["second_input"] |
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| 77 | |||
| 78 | new_h = self._hidden_act(z) |
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| 79 | # Apply mask |
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| 80 | if "mask" in sequence_map: |
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| 81 | mask = sequence_map["mask"].dimshuffle(0, 'x') |
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| 82 | new_h = mask * new_h + (1 - mask) * h |
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| 83 | return new_h |
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| 84 | |||
| 85 | def produce_input_sequences(self, x, mask=None, second_input=None): |
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| 86 | self._sequence_map.clear() |
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| 87 | if self._input_type == "sequence": |
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| 88 | self._sequence_map["x"] = T.dot(x, self.W_i) |
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| 89 | # Mask |
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| 90 | if mask: |
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| 91 | # (batch) |
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| 92 | self._sequence_map["mask"] = mask |
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| 93 | elif self._mask: |
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| 94 | # (time, batch) |
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| 95 | self._sequence_map["mask"] = self._mask |
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| 96 | # Second input |
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| 97 | if second_input: |
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| 98 | self._sequence_map["second_input"] = T.dot(second_input, self.W_i2) |
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| 99 | elif self._second_input: |
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| 100 | self._sequence_map["second_input"] = T.dot(self._second_input, self.W_i2) |
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| 101 | return self._sequence_map.values() |
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| 102 | |||
| 103 | def produce_initial_states(self, x): |
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| 104 | h0 = T.alloc(np.cast[FLOATX](0.), x.shape[0], self._hidden_size) |
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| 105 | if self._input_type == "sequence": |
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| 106 | if self.persistent_state: |
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| 107 | h0 = self.state |
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| 108 | else: |
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| 109 | h0 = x |
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| 110 | return [h0] |
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| 111 | |||
| 112 | def output(self, x): |
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| 113 | if self._input_type == "sequence": |
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| 114 | # Move middle dimension to left-most position |
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| 115 | # (sequence, batch, value) |
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| 116 | sequences = self.produce_input_sequences(x.dimshuffle((1,0,2))) |
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| 117 | else: |
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| 118 | sequences = self.produce_input_sequences(None) |
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| 119 | |||
| 120 | step_outputs = self.produce_initial_states(x) |
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| 121 | hiddens, _ = theano.scan(self.step, sequences=sequences, outputs_info=step_outputs, |
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| 122 | n_steps=self._steps, go_backwards=self._go_backwards) |
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| 123 | |||
| 124 | # Save persistent state |
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| 125 | if self.persistent_state: |
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| 126 | self.register_updates((self.state, hiddens[-1])) |
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| 127 | |||
| 128 | if self._output_type == "one": |
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| 129 | return hiddens[-1] |
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| 130 | elif self._output_type == "sequence": |
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| 131 | return hiddens.dimshuffle((1,0,2)) |
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| 132 | |||
| 133 | def prepare(self): |
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| 134 | if self._input_type == "one" and self.input_dim != self._hidden_size: |
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| 135 | raise Exception("For RNN receives one vector as input, " |
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| 136 | "the hidden size should be same as last output dimension.") |
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| 137 | self._setup_params() |
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| 138 | self._setup_functions() |
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| 139 | |||
| 140 | def _setup_functions(self): |
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| 141 | self._hidden_act = build_activation(self._hidden_activation) |
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| 142 | |||
| 143 | def _setup_params(self): |
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| 144 | if not self._vector_core: |
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| 145 | self.W_h = self.create_weight(self._hidden_size, self._hidden_size, suffix="h", initializer=self._hidden_init) |
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| 146 | else: |
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| 147 | self.W_h = self.create_bias(self._hidden_size, suffix="h") |
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| 148 | self.W_h.set_value(self.W_h.get_value() + self._vector_core) |
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| 149 | self.B_h = self.create_bias(self._hidden_size, suffix="h") |
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| 150 | |||
| 151 | self.register_parameters(self.W_h, self.B_h) |
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| 152 | |||
| 153 | if self.persistent_state: |
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| 154 | self.state = self.create_matrix(self.batch_size, self._hidden_size, "rnn_state") |
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| 155 | self.register_free_parameters(self.state) |
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| 156 | else: |
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| 157 | self.state = None |
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| 158 | |||
| 159 | if self._input_type == "sequence": |
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| 160 | self.W_i = self.create_weight(self.input_dim, self._hidden_size, suffix="i", initializer=self._input_init) |
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| 161 | self.register_parameters(self.W_i) |
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| 162 | if self._second_input_size: |
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| 163 | self.W_i2 = self.create_weight(self._second_input_size, self._hidden_size, suffix="i2", initializer=self._input_init) |
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| 164 | self.register_parameters(self.W_i2) |
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| 165 |
