def continue_run_rand_nn(matrixA, matrixB, num_gene, num_pathway, layer1,
layer2, layer3, path, dir_opt, RNA_seq_filename,
input_num, epoch, batch_size, verbose):
# RECONSTRCUT TO BE TRAINED MODEL
model = RandNN().keras_rand_nn(matrixA, matrixB, num_gene, layer0, layer1,
layer2, layer3)
with open(path + '/layer_bias_list.txt', 'rb') as filebias:
layer_bias_list = pickle.load(filebias)
with open(path + '/layer_weight_list.txt', 'rb') as fileweight:
layer_weight_list = pickle.load(fileweight)
model.compile(loss='mean_squared_error',
optimizer='adam',
metrics=['mse', 'accuracy'])
xTmp, yTmp = LoadData(dir_opt, RNA_seq_filename).load_train(0, 1)
model.fit(xTmp, yTmp, epochs=1, validation_split=1, verbose=0)
model_layer_list = []
num_layer = len(model.layers)
for i in range(num_layer):
each_layer_list = [layer_weight_list[i], layer_bias_list[i]]
model_layer_list.append(each_layer_list)
model.layers[i].set_weights(each_layer_list)
# AUTO UPDATE WEIGHT
model, history, num_layer, path = RunRandNN(
model, dir_opt, RNA_seq_filename).train(input_num, epoch, batch_size,
verbose)
return model, history, path
def run_rand_nn(model, dir_opt, RNA_seq_filename, matrixA, matrixB, input_num,
epoch, batch_size, verbose):
# AUTO UPDATE WEIGHT
model, history, num_layer, path = RunRandNN(
model, dir_opt, RNA_seq_filename).train(input_num, epoch, batch_size,
verbose)
return model, history, path
def manual_test_rand_nn(matrixA, matrixB, num_gene, num_pathway, layer1, layer2, layer3, path, dir_opt, learning_rate):
# MANUAL REBUILD THE MODEL
input_model, gene_model, pathway_model, model = build_rand_nn(matrixA, matrixB, num_gene, num_pathway, layer1, layer2, layer3)
with open(path + '/layer_list.txt', 'rb') as filelayer:
layer_list = pickle.load(filelayer)
model.compile(loss='mean_squared_error',
optimizer=Adam(lr=learning_rate),
metrics=['mse', 'accuracy'])
xTmp, yTmp = LoadData(dir_opt).load_train(0, 1)
model.fit(xTmp, yTmp, epochs = 1, validation_split = 1, verbose = 0)
num_layer = len(model.layers)
for i in range(num_layer):
model.get_layer(index = i).set_weights(layer_list[i])
# PREDICT MODEL USING [xTe, yTe]
verbose = 1
y_pred, score = RunRandNN(model, dir_opt).test(verbose, path)
def manual_test_rand_nn(matrixA, matrixB, num_gene, num_pathway, layer1,
layer2, layer3, path, dir_opt, RNA_seq_filename):
# RECONSTRCUT TEST MODEL
model = RandNN().keras_rand_nn(matrixA, matrixB, num_gene, layer0, layer1,
layer2, layer3)
with open(path + '/layer_bias_list.txt', 'rb') as filebias:
layer_bias_list = pickle.load(filebias)
with open(path + '/layer_weight_list.txt', 'rb') as fileweight:
layer_weight_list = pickle.load(fileweight)
model.compile(loss='mean_squared_error',
optimizer='adam',
metrics=['mse', 'accuracy'])
xTmp, yTmp = LoadData(dir_opt, RNA_seq_filename).load_train(0, 1)
model.fit(xTmp, yTmp, epochs=1, validation_split=1, verbose=0)
model_layer_list = []
num_layer = len(model.layers)
for i in range(num_layer):
each_layer_list = [layer_weight_list[i], layer_bias_list[i]]
model_layer_list.append(each_layer_list)
model.layers[i].set_weights(each_layer_list)
# PREDICT MODEL USING [xTe, yTe]
verbose = 1
y_pred, score = RunRandNN(model, dir_opt,
RNA_seq_filename).test(verbose, path)
def auto_test_rand_nn(model, dir_opt, RNA_seq_filename, verbose, path):
y_pred, score = RunRandNN(model, dir_opt,
RNA_seq_filename).test(verbose, path)
def auto_test_rand_nn(model, dir_opt, verbose, path):
y_pred, score = RunRandNN(model, dir_opt).test(verbose, path)
def run_rand_nn(model, dir_opt, matrixA, matrixB, input_num, epoch, batch_size, verbose, learning_rate, end_epoch):
model, history, path = RunRandNN(model, dir_opt).train(input_num, epoch, batch_size, verbose, learning_rate, end_epoch)
return model, history, path
def auto_test_rand_nn(model, dir_opt, RNA_seq_filename, verbose, path):
# GET MODEL IMMEDIATELY FROM TRAINED MODEL
y_pred, score = RunRandNN(model, dir_opt,
RNA_seq_filename).test(verbose, path)