def __init__(self, wv_matrix, lstm_hidden_dim, nn_hidden_dim, num_classes,
mean_pool=False):
print 'Initializing embedding model...'
self.mean_pool = mean_pool
input_dim = wv_matrix.shape[0]
self.embeddingLayer = layers.embeddingLayer(wv_matrix)
self.LSTMLayer = layers.LSTMLayer(input_dim, lstm_hidden_dim)
nn_input_dim = 2 * lstm_hidden_dim
self.fc1 = layers.FullyConnected(input_dim=nn_input_dim,
output_dim=nn_hidden_dim,
activation='relu')
self.fc2 = layers.FullyConnected(input_dim=nn_hidden_dim,
output_dim=nn_hidden_dim,
activation='relu')
self.linear_layer = layers.FullyConnected(input_dim=nn_hidden_dim,
output_dim=num_classes,
activation=None)
self.layers = {'embeddingLayer': self.embeddingLayer, 'lstm': self.LSTMLayer,
'fc1': self.fc1, 'fc2': self.fc2, 'linear': self.linear_layer}
self.params = self.embeddingLayer.params + self.LSTMLayer.params + \
self.fc1.params + self.fc2.params + self.linear_layer.params
def __init__(self, embeddings, lstm_hidden_dim):
print 'Initializing attention model...'
# self.reverse = reverse
input_dim = 2 * embeddings.shape[0]
self.embeddingLayer = layers.embeddingLayer(embeddings)
self.LSTMLayer = layers.LSTMLayer(input_dim, lstm_hidden_dim)
self.linear_layer = layers.FullyConnected(input_dim=lstm_hidden_dim,
output_dim=2,
activation=None)
self.layers = {'lstm': self.LSTMLayer, 'linear': self.linear_layer,
'embeddings': self.embeddingLayer}
self.params = self.LSTMLayer.params + self.linear_layer.params + self.embeddingLayer.params