num_negatives = args.num_neg
learner = args.learner
learning_rate = args.lr
batch_size = args.batch_size
epochs = args.epochs
verbose = args.verbose
topK = 1
evaluation_threads = 1 # mp.cpu_count()
print("MLP arguments: %s " % (args))
model_out_file = 'Pretrain/savedmodel'
# Loading data
t1 = time()
dataset = Dataset(args.path + args.dataset)
train, testRatings, testNegatives = dataset.trainMatrix, dataset.testRatings, dataset.testNegatives
num_users, num_items = train.shape
print("Load data done [%.1f s]. #user=%d, #item=%d, #train=%d, #test=%d"
% (time() - t1, num_users, num_items, train.nnz, len(testRatings)))
# Build model
model = get_model(num_users, num_items, layers, reg_layers)
#model.load_weights('Pretrain/savedmodel')
if learner.lower() == "adagrad":
model.compile(optimizer=Adagrad(lr=learning_rate), loss='binary_crossentropy')
elif learner.lower() == "rmsprop":
model.compile(optimizer=RMSprop(lr=learning_rate), loss='binary_crossentropy')
elif learner.lower() == "adam":
model.compile(optimizer=Adam(lr=learning_rate), loss='binary_crossentropy')
else:
layers = eval(args.layers)
reg_layers = eval(args.reg_layers)
num_negatives = args.num_neg
learner = args.learner
learning_rate = args.lr
batch_size = args.batch_size
epochs = args.epochs
verbose = args.verbose
evaluation_threads = 1 # mp.cpu_count()
print("MLP arguments: %s " % (args))
model_out_file = '%s_MLP_%s_%d.h5' % (args.dataset, args.layers, time())
# Loading data
t1 = time()
dataset = Dataset(dataset)
train, testRatings, testNegatives = dataset.trainMatrix, dataset.testRatings, dataset.testNegatives
num_users, num_items = train.shape
print("Load data done [%.1f s]. #user=%d, #item=%d, #train=%d, #test=%d"
% (time() - t1, num_users, num_items, train.nnz, len(testRatings)))
# Build model
model = get_model(num_users, num_items, layers, reg_layers)
if learner.lower() == "adagrad":
model.compile(optimizer=Adagrad(lr=learning_rate), loss='binary_crossentropy')
elif learner.lower() == "rmsprop":
model.compile(optimizer=RMSprop(lr=learning_rate), loss='binary_crossentropy')
elif learner.lower() == "adam":
model.compile(optimizer=Adam(lr=learning_rate), loss='binary_crossentropy')
else:
model.compile(optimizer=SGD(lr=learning_rate), loss='binary_crossentropy')