What is the problem that causes overfitting in the code?
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from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from keras import models
from keras.layers import Dense
from keras.regularizers import l1
from keras.layers import Activation
from keras.layers import Dropout
from sklearn.preprocessing import StandardScaler
std=StandardScaler();
x_train, x_test, y_train, y_test=train_test_split(features,target,test_size=0.2,stratify=target,random_state=1)
X_train_std=std.fit_transform(x_train)
X_test_std=std.transform(x_test)
network = models.Sequential()
network.add(Dropout(0.2, input_shape=(55,)))
network.add(Dense(units=16, activation='linear', activity_regularizer=l1(0.0001)))
network.add(Activation('relu'))
network.add(Dropout(0.2))
network.add(Dense(units=32, activation='linear', activity_regularizer=l1(0.0001)))
network.add(Activation('relu'))
network.add(Dropout(0.2))
network.add(Dense(units=1, activation='sigmoid'))
network.compile(loss=binary_crossentropy,optimizer=adam,metrics=[accuracy])
history=network.fit(X_train_std,y_train,epochs=100,batch_size=10,validation_data=(x_test, y_test))
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Topic overfitting dropout regularization neural-network machine-learning
Category Data Science