嗨，我制作了一個神經網路，我需要進行交叉驗證。我不知道是怎么做到的，特別是如何訓練或做到的。
如果有人知道，請寫信或給我一些指示。
這是我的代碼：
###Division Train / Test
X = df.drop('Peso secado',axis=1) #Variables de entrada, menos la variable de salida
y = df['Peso secado'] #Variable de salida
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=101)
###
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_train= scaler.fit_transform(X_train)
X_train
X_test = scaler.transform(X_test)
X_test
###Creacion del modelo###
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
from tensorflow.keras.optimizers import Adam
import tensorflow as tf
model = Sequential()
num_neuronas = 50
model.add(tf.keras.layers.Dense(units=6, activation='sigmoid', input_shape=(6, )))
model.add(Dense(num_neuronas,activation='relu'))
model.add(tf.keras.layers.Dense(units=1, activation='linear'))
#Buscar mejor funcion de activacion para capa de salida sigmoid? o linear?
model.summary()
model.compile(optimizer='adam',loss='mse')
###Entrenamiento###
model.fit(x = X_train, y = y_train.values,
validation_data=(X_test,y_test.values), batch_size=10, epochs=1000)
losses = pd.DataFrame(model.history.history)
losses
losses.plot()
###Evaluacion###
from sklearn.metrics import mean_squared_error,mean_absolute_error,explained_variance_score,mean_absolute_percentage_error
X_test
predictions = model.predict(X_test)
mean_absolute_error(y_test,predictions)
mean_absolute_percentage_error(y_test,predictions)
mean_squared_error(y_test,predictions)
explained_variance_score(y_test,predictions)
mean_absolute_error(y_test,predictions)/df['Peso secado'].mean()
mean_absolute_error(y_test,predictions)/df['Peso secado'].median()
一些培訓或驗證的建議會有所幫助
uj5u.com熱心網友回復：
我的第一個觀察是代碼非常丑陋且非結構化。您應該匯入代碼頂部的模塊
要執行交叉驗證，首先從 sklearn 匯入模塊（以及您需要的所有其他模塊）
from sklearn.model_selection import StratifiedKFold
我將模型定義放在一個單獨的函式中：
def get_model():
model = Sequential()
model.add(Dense(4, input_dim=8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')
return model
定義您的變數，如果您正在使用 tensorflow / Keras，請執行以下操作：
BATCH_SIZE = 64 # 128
EPOCHS = 100
k = 10
# Use stratified k-fold if the data is imbalanced
kf = StratifiedKFold(n_splits=k, shuffle=False, random_state=None)
# here comes the Cross validation
fold_index = 1
for train_index, test_index in kf.split(X, y):
X_train = X[train_index]
y_train = y[train_index]
X_test = X[test_index]
y_test = y[test_index]
# fit the model on the training set
model = get_model()
model.fit(
X_train,
y_train,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
verbose=0,
validation_data=(X_test, y_test),
)
# predict values
# pred_values = model.predict(X_test)
pred_values_prob = np.array(model(X_test))
注意：在使用 tensorflow 時，您需要在每次回圈中定義一個新模型。這不是 sklearn 的情況，因為 sklearn 在呼叫時以新的初始化權重開始。在這里，您需要單獨執行此操作。
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