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| positive_gradient_matrix = tf.multiply(input_matrix, self._hidden_states) | |
| negative_gradient_matrix = tf.multiply(self._visible_cdstates, self._hidden_cdstates) | |
| new_weights = self._weights | |
| new_weights.assign_add(tf.multiply(positive_gradient_matrix, self._leraning_rate)) | |
| new_weights.assign_sub(tf.multiply(negative_gradient_matrix, self._leraning_rate)) | |
| self._training = tf.assign(self._weights, new_weights) |
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