return (features, y)
build_batch = (BuildBatch(BATCH_SIZE).by(0, 'vector',
float).by(1, 'number', float))
if NET_ARCH == 'deep_net':
model = deep_net(input_shape=(feature_dim, ))
opti = Adam(lr=0.0001, beta_1=0.5)
elif NET_ARCH == 'DNN':
model = DNN(input_shape=(feature_dim, ))
opti = sgd(lr=0.01, momentum=0.9, clipnorm=1.0)
else:
sys.exit("Network not defined correctly, check NET_ARCH. ")
model.compile(optimizer=opti,
loss='mean_squared_error',
metrics=[Rsqured])
def train_network_batch(sample):
tloss = model.train_on_batch(sample[0], sample[1])
return (tloss[0], tloss[1])
def test_network_batch(sample):
tloss = model.test_on_batch(sample[0], sample[1])
return (tloss[0], )
def predict_network_batch(sample):
return model.predict(sample[0])
scale_activators = lambda x: (x[0] * dataset_stats.loc[
dataset_name, 'std'] + dataset_stats.loc[dataset_name, 'mean'])
X_test, y_test = load_from_npz(X_data_name_2), load_from_npz(y_data_name_2)
X_train, X_test = normalize(X_train, X_test)
# %% DNN (only fully-connected layer)
dnn = DNN()
X_train = np.reshape(X_train,
(len(X_train), len(X_train[1]) * len(X_train[2]) * 3))
X_test = np.reshape(X_test,
(len(X_test), len(X_train[1]) * len(X_train[2]) * 3))
#optimization details
adam = Adam(lr=lrf, decay=lr_decay)
dnn.compile(loss='mean_squared_error', optimizer=adam, metrics=[RMSE])
for epoch in range(1, maxepoches):
if epoch % 25 == 0 and epoch > 0:
lrf /= 2
adam = Adam(lr=lrf, decay=lr_decay)
dnn.compile(loss='mean_squared_error', optimizer=adam, metrics=[RMSE])
dnn.fit(X_train,
y_train,
epochs=epoch,
initial_epoch=epoch - 1,
shuffle=True,
batch_size=batch_size,
validation_data=(X_test, y_test),
K = 10
learning_rate = 0.001
epochs = 30
batch_size = 512
# ========================== Create dataset =======================
item_fea_col, train, val, test = create_implicit_ml_1m_dataset(
file, trans_score, embed_dim, maxlen)
train_X, train_y = train
val_X, val_y = val
# ============================Build Model==========================
model = DNN(item_fea_col, maxlen, hidden_unit, activation, embed_reg)
model.summary()
# =========================Compile============================
model.compile(loss=binary_crossentropy,
optimizer=Adam(learning_rate=learning_rate))
results = []
for epoch in range(1, epochs + 1):
# ===========================Fit==============================
t1 = time()
model.fit(
train_X,
train_y,
validation_data=(val_X, val_y),
epochs=1,
batch_size=batch_size,
)
# ===========================Test==============================
t2 = time()
if epoch % 5 == 0: