TinghuiWang /
pyActLearn
| @@ 107-128 (lines=22) @@ | ||
| 104 | self.init_state: np.zeros((batch_x.shape[0], 2 * self.num_units))}) |
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| 105 | print('Step %d, training accuracy %g, loss %g' % (i, accuracy, loss)) |
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| 106 | ||
| 107 | def predict_proba(self, x, session=None, batch_size=500): |
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| 108 | """Predict probability (Softmax) |
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| 109 | """ |
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| 110 | if session is None: |
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| 111 | if self.sess is None: |
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| 112 | session = tf.Session() |
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| 113 | self.sess = session |
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| 114 | else: |
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| 115 | session = self.sess |
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| 116 | injector = BatchSequenceInjector(batch_size=batch_size, data_x=x, seq_len=self.num_steps) |
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| 117 | injector.reset() |
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| 118 | result = None |
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| 119 | while injector.num_epochs == 0: |
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| 120 | batch_x = injector.next_batch() |
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| 121 | batch_y = session.run(self.y, |
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| 122 | feed_dict={self.x: batch_x, |
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| 123 | self.init_state: np.zeros((batch_x.shape[0], 2 * self.num_units))}) |
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| 124 | if result is None: |
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| 125 | result = batch_y |
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| 126 | else: |
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| 127 | result = np.concatenate((result, batch_y), axis=0) |
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| 128 | return result |
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| 129 | ||
| 130 | def predict(self, x, session=None, batch_size=500): |
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| 131 | if session is None: |
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| @@ 130-149 (lines=20) @@ | ||
| 127 | result = np.concatenate((result, batch_y), axis=0) |
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| 128 | return result |
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| 129 | ||
| 130 | def predict(self, x, session=None, batch_size=500): |
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| 131 | if session is None: |
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| 132 | if self.sess is None: |
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| 133 | session = tf.Session() |
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| 134 | self.sess = session |
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| 135 | else: |
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| 136 | session = self.sess |
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| 137 | injector = BatchSequenceInjector(batch_size=batch_size, data_x=x, seq_len=self.num_steps) |
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| 138 | injector.reset() |
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| 139 | result = None |
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| 140 | while injector.num_epochs == 0: |
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| 141 | batch_x = injector.next_batch() |
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| 142 | batch_y = session.run(self.y_class, |
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| 143 | feed_dict={self.x: batch_x, |
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| 144 | self.init_state: np.zeros((batch_x.shape[0], 2 * self.num_units))}) |
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| 145 | if result is None: |
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| 146 | result = batch_y |
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| 147 | else: |
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| 148 | result = np.concatenate((result, batch_y), axis=0) |
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| 149 | return result |
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| 150 | ||
