def main():
hidden_unit = 64
batch_size = 32
learning_rate = 0.001
epochs = 50
with open('../Din/dataset/dataset.pkl', 'rb') as f:
train_set = np.array(pickle.load(f))
test_set = pickle.load(f)
cate_list = pickle.load(f)
user_count, item_count, cate_count, max_sl = pickle.load(f)
train_user, train_item, train_hist, train_sl, train_y = input_data(
train_set, max_sl)
# Tensorboard
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
log_dir = 'logs/' + current_time
tensorboard = tf.keras.callbacks.TensorBoard(log_dir=log_dir,
histogram_freq=1,
write_graph=True,
write_grads=False,
write_images=True,
embeddings_freq=0,
embeddings_layer_names=None,
embeddings_metadata=None,
embeddings_data=None,
update_freq=500)
# model checkpoint
check_path = 'save/wide_deep_weights.epoch_{epoch:04d}.val_loss_{val_loss:.4f}.ckpt'
checkpoint = tf.keras.callbacks.ModelCheckpoint(check_path,
save_weights_only=True,
verbose=1,
period=1)
model = WideDeep(user_count, item_count, cate_count, cate_list,
hidden_unit)
model.summary()
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
model.compile(loss=tf.keras.losses.binary_crossentropy,
optimizer=optimizer,
metrics=[tf.keras.metrics.AUC()])
model.fit([train_user, train_item, train_hist, train_sl],
train_y,
epochs=epochs,
batch_size=batch_size,
validation_split=0.1,
callbacks=[tensorboard, checkpoint])
file=file,
read_part=read_part,
sample_num=sample_num,
embed_dim=embed_dim,
test_size=test_size)
train_X, train_y = train
test_X, test_y = test
val_X, val_y = val
# ---------------build model----------
model = WideDeep(feature_columns,
hidden_units=hidden_units,
dnn_dropout=dnn_dropout,
residual=True) #
model.summary()
# -------------model checkpoint ---------
check_path = './save/deepfm_weight.epoch_{epoch:4d}.val_loss_{val_loss:.4f}.ckpt'
checkpoint = tf.keras.callbacks.ModelCheckpoint(check_path,
save_weights_only=True,
verbose=1,
period=5)
# ------------ model evaluate ------------
METRICS = [
tf.keras.metrics.BinaryAccuracy(name='accuracy'),
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.Recall(name='recall'),
tf.keras.metrics.AUC(name='auc'),
]