def twoLayerFcEncoder_norm(bottom, hiddenDims_1, hiddenDims_z): linear1 = linearLayer("vae_encoder_1", bottom, hiddenDims_1, True) linear2 = linearLayer("vae_encoder_mu", linear1, hiddenDims_z, False) return linear2
def myModel2(samples, seqenceLengths, sequenceLength_max, outputNum): time_dim = 1 batch_dim = 0 n_classes = 20 samples_bw = tf.reverse_sequence(samples, seqenceLengths, time_dim, batch_dim, 'reverse_bottom') output_fw, output_bw, _, _ = bidirectionalLstmNetwork( 'BidirectionalLstm', samples, samples_bw, 1, [1024], seqenceLengths) output_bw_reversed = tf.reverse_sequence(output_bw, seqenceLengths, time_dim, batch_dim, 'reverse_blstm_out') biLstmOut1 = tf.concat([output_fw, output_bw_reversed], 2) biLstmOut = tf.reshape(biLstmOut1, [-1, 2 * 1024]) fc1 = linearLayer('FC_1', biLstmOut, 1024, True) fc1_dropout = tf.nn.dropout(fc1, 0.5) prediction = linearLayer('Linear_1', fc1_dropout, outputNum, False) return prediction
def oneLayerBidirectionalLstmDecoder(bottom, sequenceLengths, hiddenDim_1, hiddenDim_target): time_dim = 1 batch_dim = 0 batchSize = bottom.shape[0].value maxSeqLen = bottom.shape[1].value samples_bw = tf.reverse_sequence(bottom, sequenceLengths, time_dim, batch_dim, 'vae_decoder_reverse_bottom') output_fw, output_bw, _, _ = bidirectionalLstmNetwork( 'vae_decoder_BidirectionalLstm', bottom, samples_bw, 1, [hiddenDim_1], sequenceLengths) output_bw_reversed = tf.reverse_sequence(output_bw, sequenceLengths, time_dim, batch_dim, 'vae_decoder_reverse_blstm_out') biLstmOut1 = tf.concat([output_fw, output_bw_reversed], 2) biLstmOut = tf.reshape(biLstmOut1, [-1, 2 * hiddenDim_1]) linear_decoder_1 = linearLayer('vae_decoder_FC_1', biLstmOut, hiddenDim_1, True) reconstruction = tf.reshape(linearLayer("vae_decoder_reconstruction", linear_decoder_1, hiddenDim_target, False), shape=[-1, maxSeqLen, hiddenDim_target]) return reconstruction
def blstmSequencePredictionNetwork(samples, seqenceLengths, sequenceLength_max, outputNum, layerNum=3, neuronNum=[512]): time_dim = 1 batch_dim = 0 samples_bw = tf.reverse_sequence(samples, seqenceLengths, time_dim, batch_dim, 'reverse_bottom') output_fw, output_bw, _, _ = bidirectionalLstmNetwork( 'BidirectionalLstm', samples, samples_bw, layerNum, neuronNum, seqenceLengths) output_bw_reversed = tf.reverse_sequence(output_bw, seqenceLengths, time_dim, batch_dim, 'reverse_blstm_out') biLstmOut1 = tf.concat([output_fw, output_bw_reversed], 2) batch_size = tf.cast(tf.shape(biLstmOut1)[0], dtype=tf.int64) # Start indices for each sample index = tf.range(0, batch_size) * sequenceLength_max + (seqenceLengths - 1) # Indexing outputs = tf.gather(tf.reshape(biLstmOut1, [-1, 2 * neuronNum[-1]]), index) fc1 = linearLayer('FC_1', outputs, neuronNum[-1], True) fc1_dropout = tf.nn.dropout(fc1, 0.5) prediction = linearLayer('Linear_1', fc1_dropout, outputNum, False) return prediction
def blstmFramePredictionNetwork(samples, seqenceLengths, sequenceLength_max, outputNum, layerNum=3, neuronNum=[512]): time_dim = 1 batch_dim = 0 samples_bw = tf.reverse_sequence(samples, seqenceLengths, time_dim, batch_dim, 'reverse_bottom') output_fw, output_bw, _, _ = bidirectionalLstmNetwork( 'BidirectionalLstm', samples, samples_bw, layerNum, neuronNum, seqenceLengths) output_bw_reversed = tf.reverse_sequence(output_bw, seqenceLengths, time_dim, batch_dim, 'reverse_blstm_out') biLstmOut1 = tf.concat([output_fw, output_bw_reversed], 2) biLstmOut = tf.reshape(biLstmOut1, [-1, 2 * neuronNum[-1]]) fc1 = linearLayer('FC_1', biLstmOut, neuronNum[-1], True) fc1_dropout = tf.nn.dropout(fc1, 0.5) prediction = tf.reshape( linearLayer('Linear_1', fc1_dropout, outputNum, False), [-1, sequenceLength_max, outputNum]) return prediction
def oneLayerBidirectionalLstmEncoder(bottom, sequenceLengths, hiddenDims_1, hiddenDims_z): time_dim = 1 batch_dim = 0 batchSize = bottom.shape[0].value maxSequLen = bottom.shape[1].value samples_bw = tf.reverse_sequence(bottom, sequenceLengths, time_dim, batch_dim, 'vae_encoder_reverse_bottom') output_fw, output_bw, _, _ = bidirectionalLstmNetwork( 'vae_encoder_BidirectionalLstm', bottom, samples_bw, 1, [hiddenDims_1], sequenceLengths) output_bw_reversed = tf.reverse_sequence(output_bw, sequenceLengths, time_dim, batch_dim, 'vae_encoder_reverse_blstm_out') biLstmOut1 = tf.concat([output_fw, output_bw_reversed], 2) biLstmOut = tf.reshape(biLstmOut1, [-1, 2 * hiddenDims_1]) linear1 = linearLayer('vae_encoder_FC_1', biLstmOut, hiddenDims_1, True) mean = tf.reshape(linearLayer("vae_encoder_mu", linear1, hiddenDims_z, False), shape=[-1, maxSequLen, hiddenDims_z]) SE_ln = tf.reshape(linearLayer("vae_encoder_SE_log", linear1, hiddenDims_z, False), shape=[-1, maxSequLen, hiddenDims_z]) return mean, SE_ln
def twoLayerFcDecoder(bottom, hiddenDim_1, hiddenDim_target): linear_decoder_1 = linearLayer("vae_decoder_1", bottom, hiddenDim_1, True) reconstruction = linearLayer("vae_decoder_reconstruction", linear_decoder_1, hiddenDim_target, False) return reconstruction
def twoLayerFcEncoder(bottom, hiddenDims_1, hiddenDims_z): linear1 = linearLayer("vae_encoder_1", bottom, hiddenDims_1, True) mean = linearLayer("vae_encoder_mu", linear1, hiddenDims_z, False) SE_ln = linearLayer("vae_encoder_SE_log", linear1, hiddenDims_z, False) return mean, SE_ln