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Tegashiki model
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| DROPOUT_RATE=0.5 | |
| L2_REGULARIZATION_RATE=0.1 | |
| FEATURE_EXTRACTER_KERNEL_SIZE=7 | |
| FILTER_NUM=128 | |
| KERNEL_SIZE=5 | |
| # model_small | |
| EMBEDDING_SIZE=32 | |
| OT_HIDDEN=128 | |
| GRU_HIDDEN=128 | |
| ATTENTION_ENC_HIDDEN=64 | |
| ATTENTION_DEC_HIDDEN=64 | |
| def feature_extractor(input_stroke_t, is_training_arg): | |
| """input_stroke_t shape (batch, MAX_STROKE_NUM, MAX_ONE_STROKE_LEN, INPUT_TYPE_DIM) | |
| output: (batch, MAX_STROKE_NUM, EXTRACTED_FEATURE_DIM)""" | |
| is_training = False | |
| if(is_training_arg): | |
| is_training = None | |
| 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 = Conv1D(32, FEATURE_EXTRACTER_KERNEL_SIZE, kernel_regularizer=regularizers.l2(FE_L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(FE_L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(FE_L2_REGULARIZATION_RATE))(x) | |
| x = 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(FE_DROPOUT_RATE)(x, training=is_training) | |
| # (batch*MAX_STROKE_NUM, MAX_ONE_STROKE_LEN/2, 32) | |
| x = Conv1D(64, FEATURE_EXTRACTER_KERNEL_SIZE, kernel_regularizer=regularizers.l2(FE_L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(FE_L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(FE_L2_REGULARIZATION_RATE))(x) | |
| x = BatchNormalization()(x, training=is_training) | |
| x = Activation('relu')(x) | |
| x = MaxPooling1D(pool_size=2)(x) | |
| x = Dropout(FE_DROPOUT_RATE)(x, training=is_training) | |
| # (batch*MAX_STROKE_NUM, MAX_ONE_STROKE_LEN/4, 64) | |
| x = Conv1D(EXTRACTED_FEATURE_DIM, 7, kernel_regularizer=regularizers.l2(FE_L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(FE_L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(FE_L2_REGULARIZATION_RATE))(x) | |
| x = BatchNormalization()(x, training=is_training) | |
| x = Activation('relu')(x) | |
| x = Dropout(FE_DROPOUT_RATE)(x, training=is_training) | |
| x = GlobalMaxPooling1D()(x) | |
| x = tf.reshape(x, [-1, inpshape[1], EXTRACTED_FEATURE_DIM]) | |
| return x | |
| # dynamic shape cause TPUEstimator export to fail... | |
| def myembedding(input, num_classes, embedding_size, seq_num, name): | |
| with tf.variable_scope(name, reuse=tf.AUTO_REUSE): | |
| randinitializer = lambda: tf.random_uniform([num_classes, embedding_size], -0.05, 0.05) | |
| embedmat = tf.get_variable(name, initializer = randinitializer) | |
| onehot = tf.one_hot(input, num_classes) | |
| flatten_onehot = tf.reshape(onehot, [-1, num_classes]) | |
| return tf.reshape(tf.matmul(flatten_onehot, embedmat), [-1, seq_num, embedding_size]) | |
| def embed_stroke(stroke_features): | |
| pos_stroke = tf.range( | |
| 0, | |
| tf.shape(stroke_features)[1], | |
| delta=1, | |
| dtype=tf.int32, | |
| name='range') | |
| pos_stroke = tf.expand_dims(pos_stroke, axis=0) | |
| pos_stroke_embed = myembedding(pos_stroke, MAX_STROKE_NUM, EXTRACTED_FEATURE_DIM, MAX_STROKE_NUM, "stroke_pos_embed") | |
| stroke_pos_embedded = stroke_features + tf.cast(x=pos_stroke_embed, dtype=stroke_features.dtype) | |
| return stroke_pos_embedded | |
| def encConv1D(filternum, kernelsize, input): | |
| return Conv1D(filternum, kernelsize, activation='relu', padding='same', kernel_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(L2_REGULARIZATION_RATE))(input) | |
| def encSelfAttenBlock(input): | |
| context_vec = attention_context(input, input, MAX_STROKE_NUM) | |
| attenres = tf.contrib.layers.layer_norm(input+context_vec) | |
| x = encConv1D(2048, 1, attenres) | |
| x = encConv1D(512, 1, x) | |
| return tf.contrib.layers.layer_norm(attenres+x) | |
| def encoder_SelfAttention(input): | |
| x = encConv1D(512, 1, input) | |
| x = encSelfAttenBlock(x) | |
| x = encSelfAttenBlock(x) | |
| x = encSelfAttenBlock(x) | |
| x = encSelfAttenBlock(x) | |
| x = encSelfAttenBlock(x) | |
| x = encSelfAttenBlock(x) | |
| return x | |
| def embed_decoder(decoder_input_t): | |
| dec_input_embedded = myembedding(decoder_input_t, VOCAB_SIZE, EMBEDDING_SIZE, MAX_TOKEN_LEN, "dec_embed") | |
| dec_pos_input = tf.range( | |
| 0, | |
| tf.shape(decoder_input_t)[1], | |
| delta=1, | |
| dtype=tf.int32, | |
| name='range') | |
| dec_pos_input = tf.expand_dims(dec_pos_input, axis=0) | |
| dec_pos_embed = myembedding(dec_pos_input, MAX_TOKEN_LEN, EMBEDDING_SIZE, MAX_TOKEN_LEN, "dec_pos_embed") | |
| dec_embedded = dec_input_embedded + tf.cast(x=dec_pos_embed, dtype=dec_input_embedded.dtype) | |
| return dec_embedded | |
| def attention_context(ht_enc, ht_dec, maxtklen): | |
| w1 = Dense(ATTENTION_ENC_HIDDEN, kernel_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(L2_REGULARIZATION_RATE))(ht_enc) | |
| w2 = Dense(ATTENTION_DEC_HIDDEN, kernel_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(L2_REGULARIZATION_RATE))(ht_dec) | |
| w2_widen = tf.expand_dims(w2, axis=1) | |
| w1_widen = tf.expand_dims(w1, axis=2) | |
| w1_widen_repeat = K.repeat_elements(w1_widen, rep=maxtklen, axis=2) | |
| score =tf.nn.tanh(w1_widen_repeat+w2_widen) | |
| prob = Dense(1, activation="softmax", kernel_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(L2_REGULARIZATION_RATE))(score) | |
| ht_enc_repeated = K.repeat_elements(tf.expand_dims(ht_enc, axis=2), rep=maxtklen, axis=2) | |
| context_vec = tf.reduce_sum(prob*ht_enc_repeated, axis=1) | |
| return context_vec | |
| def decoder_CnnWithAttentionBlock(dec_input, ht_enc, is_training): | |
| x = Conv1D(FILTER_NUM, KERNEL_SIZE, activation='relu', padding='causal', kernel_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(L2_REGULARIZATION_RATE))(dec_input) | |
| # This will cause future information leak! | |
| # x = tf.contrib.layers.layer_norm(x) | |
| ht_dec = SpatialDropout1D(DROPOUT_RATE)(x, training=is_training) | |
| context_vec = attention_context(ht_enc, ht_dec, MAX_TOKEN_LEN) | |
| ht_with_cont = Concatenate()([ht_dec, context_vec]) | |
| pw_conved = Conv1D(1024, 1, activation='relu', padding='causal', kernel_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(L2_REGULARIZATION_RATE))(ht_with_cont) | |
| return SpatialDropout1D(DROPOUT_RATE)(pw_conved, training=is_training) | |
| SCALE = math.sqrt(0.5) | |
| def create_model(input_stroke_t, decoder_input_t, is_training): | |
| stroke_features = feature_extractor(input_stroke_t, is_training) | |
| stroke_embedded = embed_stroke(stroke_features) | |
| dec_embedded = embed_decoder(decoder_input_t) | |
| ht_enc = encoder_CNN(stroke_embedded, is_training) | |
| dec_ht = decoder_CnnWithAttentionBlock(dec_embedded, ht_enc, is_training) | |
| ot = Dense(OT_HIDDEN, activation="tanh", kernel_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(L2_REGULARIZATION_RATE))(dec_ht) | |
| logit = TimeDistributed(Dense(VOCAB_SIZE, kernel_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), bias_regularizer=regularizers.l2(L2_REGULARIZATION_RATE), activity_regularizer=regularizers.l2(L2_REGULARIZATION_RATE)))(ot) | |
| return logit |
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convencdec_rescon_mulsqrt