def main(learning_rate, epochs, hidden_units):
"""
feature_columns is a list and contains two dict：
- dense_features: {feat: dense_feature_name}
- sparse_features: {feat: sparse_feature_name, feat_num: the number of this feature,
embed_dim: the embedding dimension of this feature }
train_X: [dense_train_X, sparse_train_X]
test_X: [dense_test_X, sparse_test_X]
"""
feature_columns, train_X, test_X, train_y, test_y = create_dataset()
# ============================Build Model==========================
model = DCN(feature_columns, hidden_units)
model.summary()
# =============================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/dcn_weights.epoch_{epoch:04d}.val_loss_{val_loss:.4f}.ckpt'
checkpoint = tf.keras.callbacks.ModelCheckpoint(check_path, save_weights_only=True,
verbose=1, period=4)
# =========================Compile============================
model.compile(loss=binary_crossentropy, optimizer=Adam(learning_rate=learning_rate),
metrics=[AUC()])
# ===========================Fit==============================
model.fit(
train_X,
train_y,
epochs=epochs,
callbacks=[tensorboard, checkpoint],
batch_size=128,
validation_split=0.2
)
# ===========================Test==============================
print('test AUC: %f' % model.evaluate(test_X, test_y)[1])
embed_dim=embed_dim,
read_part=read_part,
sample_num=sample_num,
test_size=test_size)
train_X, train_y = train
test_X, test_y = test
# ============================Build Model==========================
model = DCN(feature_columns, hidden_units, dnn_dropout=dnn_dropout)
model.summary()
# ============================model checkpoint======================
# check_path = 'save/dcn_weights.epoch_{epoch:04d}.val_loss_{val_loss:.4f}.ckpt'
# checkpoint = tf.keras.callbacks.ModelCheckpoint(check_path, save_weights_only=True,
# verbose=1, period=5)
# =========================Compile============================
model.compile(loss=binary_crossentropy,
optimizer=Adam(learning_rate=learning_rate),
metrics=[AUC()])
# ===========================Fit==============================
model.fit(
train_X,
train_y,
epochs=epochs,
callbacks=[
EarlyStopping(monitor='val_auc',
patience=2,
restore_best_weights=True)
], # checkpoint
batch_size=batch_size,
validation_split=0.1)
# ===========================Test==============================
print('test AUC: %f' % model.evaluate(test_X, test_y)[1])