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| DROPOUT_RATE=0.5 | |
| L2_REGULARIZATION_RATE=0.1 | |
| EXTRACTED_FETURE_DIM=256 | |
| FEATURE_EXTRACTER_KERNEL_SIZE=7 | |
| GRU_HIDDEN=256 | |
| # tpu_model_fn also modify for update moving avg. | |
| def fe_conv1d(filternum, kernelsize, x): | |
| return Conv1D(filternum, kernelsize, kernel_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(L2_REGULARIZATION_RATE))(x) | |
| def feature_extractor(input_stroke_t, is_training): | |
| """input_stroke_t shape (batch, MAX_STROKE_NUM, MAX_ONE_STROKE_LEN, INPUT_TYPE_DIM)""" | |
| with tf.variable_scope("feature_extractor"): | |
| inpshape = input_stroke_t.shape | |
| x = tf.reshape(input_stroke_t, [-1, inpshape[2], inpshape[3]]) | |
| # (batch*MAX_STROKE_NUM, MAX_ONE_STROKE_LEN, INPUT_TYPE_DIM) | |
| x = fe_conv1d(32, FEATURE_EXTRACTER_KERNEL_SIZE, x) | |
| x = tf.layers.BatchNormalization()(x, training=is_training) | |
| x = Activation('relu')(x) | |
| # (batch*MAX_STROKE_NUM, MAX_ONE_STROKE_LEN, 32) | |
| x = MaxPooling1D(pool_size=2)(x) | |
| x = Dropout(DROPOUT_RATE)(x, training=is_training) | |
| # (batch*MAX_STROKE_NUM, MAX_ONE_STROKE_LEN/2, 32) | |
| x = fe_conv1d(64, FEATURE_EXTRACTER_KERNEL_SIZE, x) | |
| x = tf.layers.BatchNormalization()(x, training=is_training) | |
| x = Activation('relu')(x) | |
| x = MaxPooling1D(pool_size=2)(x) | |
| x = Dropout(DROPOUT_RATE)(x, training=is_training) | |
| # (batch*MAX_STROKE_NUM, MAX_ONE_STROKE_LEN/4, 64) | |
| x = fe_conv1d(EXTRACTED_FETURE_DIM, FEATURE_EXTRACTER_KERNEL_SIZE, x) | |
| x = tf.layers.BatchNormalization()(x, training=is_training) | |
| x = Activation('relu')(x) | |
| x = Dropout(DROPOUT_RATE)(x, training=is_training) | |
| x = GlobalMaxPooling1D()(x) | |
| x = tf.reshape(x, [-1, inpshape[1], EXTRACTED_FETURE_DIM]) | |
| return x | |
| def create_model(input_stroke_t, is_training): | |
| features = feature_extractor(input_stroke_t, is_training) | |
| last = GRU(GRU_HIDDEN, dropout=DROPOUT_RATE, recurrent_dropout=DROPOUT_RATE, kernel_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), recurrent_regularizer=regularizers.l2(L2_REGULARIZATION_RATE))(features, training=is_training) | |
| if L2_REGULARIZATION_RATE == 0.0: | |
| logit = Dense(VOCAB_SIZE)(last) | |
| else: | |
| logit = Dense(VOCAB_SIZE, kernel_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(L2_REGULARIZATION_RATE))(last) | |
| return logit |
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