def __init__(self, shape, ActivFun):
"""
Input:
shape: a tuple, the length represents the number of layers,
its elements represent the number of nodes/neurons
at the corresponding layer
ActivFun: a list, elements of a name of an activation
function or an activation function itself
Desc:
Inside the class we create the list of all hidden layers
+ output layer, an elements of this list
are objects of Layer class
"""
self.shape = np.array(shape)
self.size = self.shape.size
if len(ActivFun) == 1:
self.ActivFun = [ActivFun[0] for k in range(self.size - 1)]
elif len(ActivFun) == 2:
self.ActivFun = [ActivFun[0]
for k in range(self.size - 2)] + [ActivFun[-1]]
else:
assert len(ActivFun) == self.size - 1
self.ActivFun = ActivFun
self.layers = [
Dense((shape[k - 1], shape[k]), self.ActivFun[k - 1])
for k in range(1, self.size - 1)
] + [OutputLayer((shape[-2], shape[-1]), self.ActivFun[-1])]
def __init__(self):
types, groundTruths, dataVectors = self.getData()
# create all layers
self.inputLayer = InputLayer(len(dataVectors[0]))
self.hiddenLayer = HiddenLayer(len(dataVectors[0]), self.numOfHiddens,
"lrelu")
self.outputLayer = OutputLayer(self.numOfHiddens, len(types), "lrelu")
t0 = time.time()
self.trainNetwork(types, groundTruths, dataVectors)
t1 = time.time()
print("\nTime: " + str(t1 - t0))
print(self.hiddenLayer.weights)
print("------------------------------------")
print(self.outputLayer.weights)
def create_output_layer(self, vocab, domain_size, hidden_size):
return OutputLayer(vocab, domain_size, hidden_size)
#print(epoch) # to avoid unused variable error; delete later
# testing
np.random.seed(1)
# data
x = np.random.rand(1, 10)
y = np.random.rand(1, 3)
# network
NN = NeuralNetwork(x, y)
NN.add(Inputlayer(x.shape, (1, 5)))
NN.add(HiddenLayer((1, 5), (1, 9), tanh))
NN.add(HiddenLayer((1, 9), (1, 3), tanh))
NN.add(OutputLayer((1, 3), y.shape))
# feedforward
output1 = NN.layers[0].feedforward(x)
output2 = NN.layers[1].feedforward(output1)
output3 = NN.layers[2].feedforward(output2)
output4 = NN.layers[3].feedforward(output3)
#print(NN.fit(x,y,1,1))
"""
print("outout1",output1)
print("outout2",output2)
print("outout3",output3)
print("outout4",output4)
"""
def __init__(self, max_context_length, max_query_length):
self.max_context_length = max_context_length
self.max_query_length = max_query_length
self.settings = {
'window_size': 2,
'n': 100,
'epochs': 5,
'learning_rate': 0.0001
}
self.extract_training_inputs(
"Question Answering System\\Training and testing data")
#input_h = tf.keras.Input(shape = (self.max_context_length, self.settings["n"]), dtype = "float32", name = "context_input")
#input_u = tf.keras.Input(shape = (self.max_query_length, self.settings["n"]), dtype = "float32", name = "query_input")
input_h = tf.keras.Input(shape=(114, self.settings["n"]),
dtype="float32",
name="context_input")
input_u = tf.keras.Input(shape=(20, self.settings["n"]),
dtype="float32",
name="query_input")
# LSTM layer for Context and for Query:
lstm_context = tf.keras.layers.LSTM(
self.settings["n"], recurrent_dropout=0, return_sequences=True
) # Define an LSTM LAYER for context matrix of size dxT
bidirectional_context_layer = tf.keras.layers.Bidirectional(
lstm_context
) # Define BiLSTM LAYER by wrapping the lstm_context LAYER
h = bidirectional_context_layer(
input_h
) # context TENSOR of size (1, T, 2d) is returned by plugging an Input tensor context_inputs
#bidirectional_context_layer_tensor = bidirectional_context_layer(in_h) # context TENSOR of size (1, T, 2d) is returned by plugging an Input tensor context_inputs
lstm_query = tf.keras.layers.LSTM(
self.settings["n"], recurrent_dropout=0, return_sequences=True
) # Define an LSTM layer for query matrix of size dxT
bidirectional_query_layer = tf.keras.layers.Bidirectional(
lstm_query) # Define BiLSTM LAYER by wrapping the lstm_query LAYER
u = bidirectional_query_layer(
input_u
) # query TENSOR of size (1, J, 2d) is returned by plugging an Input tensor query_inputs
#bidirectional_query_layer_tensor = bidirectional_query_layer(in_u) # query TENSOR of size (1, J, 2d) is returned by plugging an Input tensor query_inputs
#
#
#_____________________________ FORMING SIMILARITY MATRIX S _____________________________
# Initiate a 1x6d trainable weight vector with random weights. The shape is 1x6d since this vector will be used in multiplication with concatenated version of outputs from Context (H) and Query (U) biLSTMs: S = alpha(H, U)
similarity_matrix = Similarity()([h, u])
context2query = Context2Query()(u, similarity_matrix)
query2context = Query2Context()(h, similarity_matrix)
#megamerge = MegaMerge()(h, context2query, query2context, self.max_context_length)
megamerge = MegaMerge()(h, context2query, query2context, 114)
#
#
#_____________________________ MODELING LAYER _____________________________
G = tf.transpose(megamerge, [
0, 2, 1
]) # Transpose G to shape (?, timesteps = num_words, features = 800)
print("G after expanding dims is: ", G)
lstm_m1 = tf.keras.layers.LSTM(
self.settings["n"], recurrent_dropout=0,
return_sequences=True) # Define an LSTM LAYER for M1
bidirectional_m1_layer = tf.keras.layers.Bidirectional(
lstm_m1) # Define BiLSTM LAYER by wrapping the lstm_m1 LAYER
m1_tensor = bidirectional_m1_layer(
G
) # M1 TENSOR of size (?, T, 2d) is returned by plugging an Input tensor G
lstm_m2 = tf.keras.layers.LSTM(
self.settings["n"], recurrent_dropout=0,
return_sequences=True) # Define an LSTM LAYER for M1
bidirectional_m2_layer = tf.keras.layers.Bidirectional(
lstm_m2) # Define BiLSTM LAYER by wrapping the lstm_m1 LAYER
m2_tensor = bidirectional_m2_layer(
m1_tensor
) # M2 TENSOR of size (?, T, 2d) is returned by plugging an Input tensor m1_tensor
print("\nm1_tensor is: ", m1_tensor)
print("m2_tensor is: ", m2_tensor)
#
#
#_____________________________ OUTPUT LAYER _____________________________
# Discharge the first dimension from G, M1 and M2 because they won't be used anymore. Their shape will be (T, 8d), (T, 2d) and (T, 2d) respectively. We next transpose them to
# coherent shape of (8d, T) and (2d, T)
G = tf.transpose(G, [0, 2, 1])
m1_tensor = tf.transpose(m1_tensor, [0, 2, 1])
m2_tensor = tf.transpose(m2_tensor, [0, 2, 1])
start_end_index_pred = OutputLayer(name="output_indices")(
[G, m1_tensor, m2_tensor])
model = tf.keras.Model(inputs=[input_h, input_u],
outputs=start_end_index_pred)
sgd = tf.keras.optimizers.SGD(learning_rate=0.001)
model.compile(loss=some_loss_function,
optimizer=sgd,
metrics=[some_accuracy_metric])
self.model = model