我一直在看Heroku页面。
我发现将网站升级到2可能会提高网站的性能。
我看了看支持页面,上面写着
def data():
import pandas as pd
import feather
df_hyper_X = feather.read_dataframe('df_hyper_X_train.feather')
df_hyper_Y = feather.read_dataframe('df_hyper_Y_train.feather')
return df_hyper_X, df_hyper_Y
def hyper_model(df_hyper_X,df_hyper_Y):
stdscl_features = ['pre_grade', 'math']
normscl_features = 'time'
stdscl_transformer = Pipeline(steps=[('stdscaler', StandardScaler())])
normscl_transformer = Pipeline(steps=[('normscaler', MinMaxScaler())])
preprocessor = ColumnTransformer(transformers=[('stdscl', stdscl_transformer, stdscl_features),('minmaxscl', normscl_transformer, normscl_features)], remainder='passthrough')
metrics = [
tf.keras.metrics.TruePositives(name='tp'),
tf.keras.metrics.FalsePositives(name='fp'),
tf.keras.metrics.TrueNegatives(name='tn'),
tf.keras.metrics.FalseNegatives(name='fn'),
tf.keras.metrics.BinaryAccuracy(name='accuracy'),
tf.keras.metrics.Precision(name='precision'),
tf.keras.metrics.AUC(name='auc'),
]
model = tf.keras.Sequential()
model.add(Dense({{choice([2,4,8,16,32,64])}}, activation={{choice(['relu', 'sigmoid', 'tanh', 'elu', 'selu'])}}, kernel_initializer={{choice(['lecun_uniform','glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform'])}}
, input_shape=(16,))) #If ReLu use --> HE uniform initialization #kernel_regularizer=tf.keras.regularizers.l2({{choice([0.01, 0.05, 0.1])}}
#model.add(LeakyReLU(alpha={{uniform(0.5, 1)}}))
model.add(Dropout({{uniform(0, 1)}}))
if ({{choice(['one', 'two'])}}) == 'two':
model.add(Dense({{choice([2,4,8,16,32,64])}}, activation={{choice(['relu', 'sigmoid', 'tanh', 'elu', 'selu'])}}))
model.add(Dropout({{uniform(0, 1)}}))
#model.add(Dense({{choice([2,4,8,16,32,64])}}, activation={{choice(['relu', 'sigmoid', 'tanh', 'elu', 'selu'])}})) third hidden layer
#model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense(1, activation='sigmoid'))
adam = tf.keras.optimizers.Adam(lr={{choice([0.0001, 0.001, 0.01, 0.1])}})
nadam = tf.keras.optimizers.Nadam(lr={{choice([0.0001, 0.001, 0.01, 0.1])}})
adamax = tf.keras.optimizers.Adamax(lr={{choice([0.0001, 0.001, 0.01, 0.1])}})
adagrad = tf.keras.optimizers.Adagrad(lr={{choice([0.0001, 0.001, 0.01, 0.1])}})
adadelta = tf.keras.optimizers.Adadelta(lr={{choice([0.0001, 0.001, 0.01, 0.1])}})
sgd = tf.keras.optimizers.SGD(lr={{choice([0.0001, 0.001, 0.01, 0.1])}})
rmsprop = tf.keras.optimizers.RMSprop(lr={{choice([0.0001, 0.001, 0.01, 0.1])}})
opti_choice = {{choice(['adam', 'nadam', 'adamax','adagrad', 'adadelta', 'sgd','rmsprop'])}}
if opti_choice == 'adam':
optimizer = adam
elif opti_choice == 'nadam':
optimizer = nadam
elif opti_choice == 'adamax':
optimizer = adamax
elif opti_choice == 'adagrad':
optimizer = adagrad
elif opti_choice == 'adadelta':
optimizer = adadelta
elif opti_choice == 'sgd':
optimizer = sgd
else:
optimizer = rmsprop
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=metrics)
kfold = KFold(n_splits=10, shuffle=True, random_state=3)
imba_pipeline = make_pipeline(preprocessor, SMOTETomek(sampling_strategy='auto', random_state=2),
KerasClassifier(model, epochs={{choice([20,30,40,50,60,70])}}, batch_size={{choice([16,32, 64, 128])}}, verbose=0))
results = cross_val_score(imba_pipeline, df_hyper_X, df_hyper_Y, cv=kfold, scoring='precision').mean()
print('Precision', results)
return {'loss': -results, 'status': STATUS_OK, 'model': model}
if __name__ == '__main__':
best_run, best_model = optim.minimize(model=hyper_model,
data=data,
algo=tpe.suggest,
max_evals=30,
trials=Trials(),
notebook_name = 'drive/My Drive/Colab Notebooks/final_NL_EU_Non-EU')
X_train, Y_train, X_test, Y_test = data()
print("Evalutation of best performing model:")
print(best_model.evaluate(X_test, Y_test))
print("Best performing model chosen hyper-parameters:")
print(best_run)
有人可以解释一下网络和工作者的确切含义,因为我对定义没有清楚的了解吗?
此外,如果我将网络设置为2,工作人员不会更改吗?