[FIXED] Keras EarlyCallBack stopping after one epoch

Issue

My dataset( Network traffic dataset where we do binary classification)-

Number of features is 25 and I have normalized the dataset.

My model-

verbose=1
epoch_number=1000
batch_size = 32

n_outputs = 1
model = Sequential()
model.add(Conv1D(filters=200, kernel_size=4, strides=3,activation='relu', input_shape=(25,1)))
model.add(Dropout(0.05))
model.add(BatchNormalization())

model.add(Conv1D(filters=200, kernel_size=5, strides=1,activation='relu', input_shape=(25,1)))
model.add(Dropout(0.05))
model.add(BatchNormalization())

model.add(MaxPooling1D(pool_size=2))
model.add(Dropout(0.05))

model.add(Flatten())

model.add(Dense(200, activation='relu'))
model.add(Dropout(0.05))

model.add(Dense(100, activation='relu'))
model.add(Dropout(0.05))

model.add(Dense(50, activation='relu'))
model.add(Dropout(0.05))



model.add(Dense(n_outputs, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam',metrics=['acc',f1_m,precision_m, recall_m])
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1)
# fit network
model.fit(X_train, y_train,validation_data=(X_test, y_test),epochs=epoch_number, batch_size=batch_size, verbose=1,callbacks=[es])
    # evaluate model
loss, accuracy, f1_score, precision, recall = model.evaluate(X_test, y_test, batch_size=batch_size, verbose=0)
print(loss,accuracy,f1_score,precision,recall)

My model is stopping after one epoch when I add Keras Earlycall back even though loss is decreasing after every epoch when I remove it.

Solution

If you had printed your logs of training of dataset without using early stopping then It would have been easier to diagnose.

Now Let's look at the possibilities. You have set EarlyStopping as mentioned below.

es = EarlyStopping(monitor='val_loss', mode='min', verbose=1)

Then that means your early stopping layer is like mentioned below which has default parameters.

tf.keras.callbacks.EarlyStopping(
    monitor="val_loss",
    min_delta=0,
    patience=0,
    verbose=1,
    mode="min",
    baseline=None,
    restore_best_weights=False,
)

Now here your patience=0 , mode='min', 'min_delta= 0' and monitor_loss = 'val_loss' This simply means that if your validation loss is not decreasing in the next layer then it will stop.

Or if your Validation loss is same or greater than the previous epoch then it will stop.

I would recommend you to change your patience parameter

Answered By - Varun Singh