hiddendim = 100
embeddingdim = 100
graph = tf.Graph()
onehot = True
inputdim = maxlength * vocabsize if onehot else maxlength * embeddingdim
# Define embeddings matrix
embeddings = Embedding(vocabsize, one_hot=onehot, embedding_size=embeddingdim)
# Input data.
dataset = tf.placeholder(tf.int32, shape=[batch_size, maxlength], name='Train')
labels = tf.placeholder(tf.float32,
shape=[batch_size, labelspace],
name='Label')
# Model
hidden_layer = Layer.W(inputdim, hiddendim, 'Hidden')
hidden_bias = Layer.b(hiddendim, 'HiddenBias')
# Prediction
output_layer = Layer.W(hiddendim, labelspace, 'Output')
output_bias = Layer.b(labelspace, 'OutputBias')
embedded = tf.reshape(embeddings.lookup(dataset), [batch_size, inputdim])
forward = tf.nn.relu(tf.matmul(embedded, hidden_layer) + hidden_bias)
dropout = tf.nn.dropout(forward, 0.5)
logits = tf.matmul(dropout, output_layer) + output_bias
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, labels))
train_op = tf.train.AdamOptimizer().minimize(loss)
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
embeddings = Embedding(vocabsize, one_hot=onehot, embedding_size=embeddingdim)
# Input -> LSTM -> Outstate
inputs = tf.placeholder(tf.int32, [batch_size, maxlength])
labels = tf.placeholder(tf.float32, [batch_size, labelspace])
lengths = tf.placeholder(tf.int32, [batch_size])
# RNN
lstm = tf.nn.rnn_cell.LSTMCell(hiddendim,
initializer=tf.contrib.layers.xavier_initializer(seed=20160501))
lstm = tf.nn.rnn_cell.DropoutWrapper(lstm, output_keep_prob=dropout)
# Prediction
output_layer = Layer.W(2*hiddendim, labelspace, 'Output')
output_bias = Layer.b(labelspace, 'OutputBias')
outputs, fstate = tf.nn.dynamic_rnn(lstm, embeddings.lookup(inputs),
sequence_length=lengths,
dtype=tf.float32)
logits = tf.matmul(fstate, output_layer) + output_bias
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, labels))
## Learning ##
## Optimizer Adam/RMSPropOptimizer tf.train.AdamOptimizer()
optimizer = tf.train.AdamOptimizer()
## Gradient Clipping:
##tvars = tf.trainable_variables()
##grads, _ = tf.clip_by_global_norm(tf.gradients(loss, tvars), 5.0)
##train_op = optimizer.apply_gradients(zip(grads, tvars))
train_op = optimizer.minimize(loss)
initializer=tf.contrib.layers.xavier_initializer(seed=20160501))
lstm[0] = tf.nn.rnn_cell.DropoutWrapper(lstm[0], output_keep_prob=dropout)
lstm[1] = tf.nn.rnn_cell.DropoutWrapper(lstm[1], output_keep_prob=dropout)
lstm[2] = tf.nn.rnn_cell.DropoutWrapper(lstm[2], output_keep_prob=dropout)
lstm[0] = tf.nn.rnn_cell.MultiRNNCell(cells=[lstm[0]] * multicells, state_is_tuple=True)
lstm[1] = tf.nn.rnn_cell.MultiRNNCell(cells=[lstm[1]] * multicells, state_is_tuple=True)
lstm[2] = tf.nn.rnn_cell.MultiRNNCell(cells=[lstm[2]] * multicells, state_is_tuple=True)
# Prediction
output_layer = {}
output_layer[0] = Layer.W(multicells*hiddendim, 20, 'Output-Sou')
output_layer[1] = Layer.W(multicells*hiddendim, 20, 'Output-Ref')
output_layer[2] = Layer.W(multicells*hiddendim, 9, 'Output-Dir')
output_bias = {}
output_bias[0] = Layer.b(20, 'OutputBias-Sou')
output_bias[1] = Layer.b(20, 'OutputBias-Ref')
output_bias[2] = Layer.b(9, 'OutputBias-Dir')
outputs = {}
fstate = {}
with tf.variable_scope("lstm0"):
outputs[0], fstate[0] = tf.nn.dynamic_rnn(lstm[0], embeddings[0].lookup(inputs),
sequence_length=lengths,
dtype=tf.float32)
with tf.variable_scope("lstm1"):
outputs[1], fstate[1] = tf.nn.dynamic_rnn(lstm[1], embeddings[1].lookup(inputs),
sequence_length=lengths,
dtype=tf.float32)
with tf.variable_scope("lstm2"):
outputs[2], fstate[2] = tf.nn.dynamic_rnn(lstm[2], embeddings[2].lookup(inputs),
inputs = tf.placeholder(tf.int32, [batch_size, maxlength], name="Utterance")
lengths = tf.placeholder(tf.int32, [batch_size], name="Lengths")
final_size = rep_dim - 5*2
## weights && Convolutions
W = {
'cl1': Layer.convW([3, 3, 20, filters]),
'cl2': Layer.convW([3, 3, filters, filters]),
'cl3': Layer.convW([3, 3, filters, filters]),
'cl4': Layer.convW([3, 3, filters, filters]),
'cl5': Layer.convW([3, 3, filters, filters]),
'out': Layer.W(final_size*final_size*filters + 2*hiddendim, rep_dim*rep_dim)
}
B = {
'cb1': Layer.b(filters, init='Normal'),
'cb2': Layer.b(filters, init='Normal'),
'cb3': Layer.b(filters, init='Normal'),
'cb4': Layer.b(filters, init='Normal'),
'cb5': Layer.b(filters, init='Normal'),
'out': Layer.b(rep_dim*rep_dim)
}
# Define embeddings matrix
embeddings = Embedding(vocabsize, one_hot=False, embedding_size=hiddendim)
# RNN
dropout = 0.75
lstm = tf.nn.rnn_cell.LSTMCell(hiddendim,
initializer=tf.contrib.layers.xavier_initializer(seed=20160501))
lstm = tf.nn.rnn_cell.DropoutWrapper(lstm, output_keep_prob=dropout)
lstm[1] = tf.contrib.rnn.DropoutWrapper(lstm[1], output_keep_prob=dropout)
lstm[2] = tf.contrib.rnn.DropoutWrapper(lstm[2], output_keep_prob=dropout)
lstm[0] = tf.contrib.rnn.MultiRNNCell(cells=[lstm[0]] * multicells,
state_is_tuple=True)
lstm[1] = tf.contrib.rnn.MultiRNNCell(cells=[lstm[1]] * multicells,
state_is_tuple=True)
lstm[2] = tf.contrib.rnn.MultiRNNCell(cells=[lstm[2]] * multicells,
state_is_tuple=True)
# Prediction
output_layer = {}
output_layer[0] = Layer.W(multicells * hiddendim, 20, 'Output-Sou')
output_layer[1] = Layer.W(multicells * hiddendim, 20, 'Output-Ref')
output_layer[2] = Layer.W(multicells * hiddendim, 9, 'Output-Dir')
output_bias = {}
output_bias[0] = Layer.b(20, 'OutputBias-Sou')
output_bias[1] = Layer.b(20, 'OutputBias-Ref')
output_bias[2] = Layer.b(9, 'OutputBias-Dir')
outputs = {}
fstate = {}
with tf.variable_scope("lstm0"):
outputs[0], fstate[0] = tf.nn.dynamic_rnn(lstm[0],
embeddings[0].lookup(inputs),
sequence_length=lengths,
dtype=tf.float32)
with tf.variable_scope("lstm1"):
outputs[1], fstate[1] = tf.nn.dynamic_rnn(lstm[1],
embeddings[1].lookup(inputs),
sequence_length=lengths,
dtype=tf.float32)
batch_size = 128 hiddendim = 100 embeddingdim = 100 graph = tf.Graph() onehot = True inputdim = maxlength*vocabsize if onehot else maxlength*embeddingdim # Define embeddings matrix embeddings = Embedding(vocabsize, one_hot=onehot, embedding_size=embeddingdim) # Input data. dataset = tf.placeholder(tf.int32, shape=[batch_size, maxlength], name='Train') labels = tf.placeholder(tf.float32, shape=[batch_size, labelspace], name='Label') # Model hidden_layer = Layer.W(inputdim, hiddendim, 'Hidden') hidden_bias = Layer.b(hiddendim, 'HiddenBias') # Prediction output_layer = Layer.W(hiddendim, labelspace, 'Output') output_bias = Layer.b(labelspace, 'OutputBias') embedded = tf.reshape(embeddings.lookup(dataset), [batch_size,inputdim]) forward = tf.nn.relu(tf.matmul(embedded, hidden_layer) + hidden_bias) dropout = tf.nn.dropout(forward, 0.5) logits = tf.matmul(dropout, output_layer) + output_bias loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, labels)) train_op = tf.train.AdamOptimizer().minimize(loss) correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1)) with tf.Session() as sess: sess.run(tf.initialize_all_variables())
Layer.convW([3, 3, 20, filters]),
'cl2':
Layer.convW([3, 3, filters, filters]),
'cl3':
Layer.convW([3, 3, filters, filters]),
'cl4':
Layer.convW([3, 3, filters, filters]),
'cl5':
Layer.convW([3, 3, filters, filters]),
'out':
Layer.W(final_size * final_size * filters + 2 * hiddendim,
rep_dim * rep_dim)
}
B = {
'cb1': Layer.b(filters, init='Normal'),
'cb2': Layer.b(filters, init='Normal'),
'cb3': Layer.b(filters, init='Normal'),
'cb4': Layer.b(filters, init='Normal'),
'cb5': Layer.b(filters, init='Normal'),
'out': Layer.b(rep_dim * rep_dim)
}
# Define embeddings matrix
embeddings = Embedding(vocabsize, one_hot=False, embedding_size=hiddendim)
# RNN
dropout = 0.75
lstm = tf.nn.rnn_cell.LSTMCell(
hiddendim, initializer=tf.contrib.layers.xavier_initializer(seed=20160501))
lstm = tf.nn.rnn_cell.DropoutWrapper(lstm, output_keep_prob=dropout)
