def build_DNN_Arch2(aa_length, nu_length, y_length):
tf.reset_default_graph()
# Build neural network - input data shape, number of words in vocabulary (size of first array element).
net_aa = tflearn.input_data(shape=[None, aa_length], name="amino")
net_nu = tflearn.input_data(shape=[None, nu_length], name="nuc")
# Two fully connected layers with 4 hidden units/neurons
# net_aa = tflearn.fully_connected(net_aa, 4)
net_nu = tflearn.fully_connected(net_nu, 4)
# number of intents, columns in the matrix train_y
net = tflearn.merge_outputs([net_aa, net_nu])
net = tflearn.fully_connected(net, 4)
net = tflearn.fully_connected(net, y_length, activation='sigmoid')
# regression to find best parameters, during training
net = tflearn.regression(net, optimizer='adam')
# Define Deep Neural Network model and setup tensorboard
model = tflearn.DNN(net, tensorboard_dir='tflearn_arch2_logs', tensorboard_verbose=3)
return model
def build_tflearn_ann(length):
input_layer = input_data(shape=[None, length, 1])
pool_layer_1 = max_pool_1d(input_layer, 10, name='pool_layer_1')
pool_layer_2 = max_pool_1d(pool_layer_1, 5, name='pool_layer_2')
pool_layer_3 = max_pool_1d(pool_layer_2, 5, name='pool_layer_3')
pool_layer_4 = max_pool_1d(pool_layer_3, 5, name='pool_layer_3')
fully_connect_1 = fully_connected(pool_layer_3,
512,
activation='relu',
name='fully_connect_1',
weights_init='xavier',
regularizer="L2")
fully_connect_2 = fully_connected(pool_layer_2,
512,
activation='relu',
name='fully_connect_2',
weights_init='xavier',
regularizer="L2")
fully_connect_3 = fully_connected(pool_layer_1,
512,
activation='relu',
name='fully_connect_3',
weights_init='xavier',
regularizer="L2")
fully_connect_4 = fully_connected(pool_layer_4,
512,
activation='relu',
name='fully_connect_3',
weights_init='xavier',
regularizer="L2")
# Merge above layers
merge_layer = tflearn.merge_outputs(
[fully_connect_1, fully_connect_2, fully_connect_3, fully_connect_4])
# merge_layer = tflearn.merge_outputs(
# [fully_connect_1, fully_connect_2, fully_connect_3, fully_connect_4, fully_connect_5])
# merge_layer = tflearn.merge_outputs(
# [fully_connect_1, fully_connect_2, fully_connect_3, fully_connect_4, fully_connect_5, fully_connect_6,
# fully_connect_7, fully_connect_8, fully_connect_9, fully_connect_10])
drop_2 = dropout(merge_layer, 0.25)
fc_layer_4 = fully_connected(drop_2,
2048,
activation='relu',
name='fc_layer_4',
regularizer='L2',
weights_init='xavier',
weight_decay=0.001)
drop_2 = dropout(fc_layer_4, drop_out_prob)
fc_layer_5 = fully_connected(drop_2,
1024,
activation='relu',
name='fc_layer_5',
regularizer='L2',
weights_init='xavier',
weight_decay=0.001)
drop_3 = dropout(fc_layer_5, drop_out_prob)
fc_layer_6 = fully_connected(drop_3,
128,
activation='relu',
name='fc_layer_5',
regularizer='L2',
weights_init='xavier',
weight_decay=0.001)
drop_4 = dropout(fc_layer_6, drop_out_prob)
# Output
fc_layer_2 = fully_connected(drop_4,
3,
activation='softmax',
name='output')
network = regression(fc_layer_2,
optimizer='adam',
loss='softmax_categorical_crossentropy',
learning_rate=0.0001,
metric='Accuracy')
model = tflearn.DNN(network)
return model