test_size=test_size)
train_X, train_y = train
test_X, test_y = test
# ============================Build Model==========================
model = WideDeep(feature_columns,
hidden_units=hidden_units,
dnn_dropout=dnn_dropout)
model.summary()
# ============================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=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_loss',
patience=1,
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])
patience=10,
mode='max',
restore_best_weights=True)
model.fit(
train_X,
train_y,
epochs=epochs,
# callbacks=[early_stopping, checkpoint],
batch_size=batch_size,
validation_split=0.1,
validation_data=(val_X, val_y),
# class_weight={0:1, 1:3}, # 样本均衡
)
print('test AUC: %f' % model.evaluate(test_X, test_y)[1])
# ------------- model evaluation in test dataset ----
train_predictions_weighted = model.predict(train_X, batch_size=batch_size)
test_predictions_weighted = model.predict(test_X, batch_size=batch_size)
# ------------- confusion matrix
from sklearn.metrics import confusion_matrix, roc_curve
import matplotlib.pyplot as plt
import seaborn as sns
def plot_cm(labels, predictions, p=0.5):
cm = confusion_matrix(labels, predictions > p)
plt.figure(figsize=(5, 5))
# ============================Build Model==========================
mirrored_strategy = tf.distribute.MirroredStrategy()
with mirrored_strategy.scope():
model = WideDeep(feature_columns,
hidden_units=hidden_units,
dnn_dropout=dnn_dropout)
model.summary()
# ============================Compile============================
model.compile(loss=binary_crossentropy,
optimizer=Adam(learning_rate=learning_rate),
metrics=[AUC()])
# ============================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=5)
# ==============================Fit==============================
model.fit(
train_X,
train_y,
epochs=epochs,
callbacks=[
EarlyStopping(monitor='val_loss',
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, batch_size=batch_size)[1])