training model for different batch sizes in keras

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I want to train my model for different batch sizes i.e: [64, 128] I am doing it with for loop like below

   epoch=2 
   batch_sizes = [128,256] 
   for i in range(len(batch_sizes)):
     history = model.fit(x_train, y_train, batch_sizes[i], epochs=epochs, 
          callbacks=[early_stopping, chk], validation_data=(x_test, y_test))

for above code my model produce following results:

    Epoch 1/2
    311/311 [==============================] - 157s 494ms/step - loss: 0.2318 - 
    f1: 0.0723 
    Epoch 2/2
    311/311 [==============================] - 152s 488ms/step - loss: 0.1402 - 
    f1: 0.4360 

    Epoch 1/2
    156/156 [==============================] - 137s 877ms/step - loss: 0.1197 - 
    f1: **0.5450** 
    Epoch 2/2
    156/156 [==============================] - 136s 871ms/step - loss: 0.1132 - 
    f1: 0.5756

it looks like the model continues training after completing training for batch size 64, i.e I want to get my model trained for the next batch from scratch, how can I do it kindly guide me. p.s: what i have tried:

   epoch=2 
   batch_sizes = [128,256] 
   for i in range(len(batch_sizes)):
     history = model.fit(x_train, y_train, batch_sizes[i], epochs=epochs, 
          callbacks=[early_stopping, chk], validation_data=(x_test, y_test))
   keras.backend.clear_session()

it also did not worked

model-fitting keras python loops
1 Answers

You can write a function to define a model, and you would need to call that before the subsequent fit calls. If your model is contained within model, the weights are updated during training, and they stay that way after the fit call. That is why you need to redefine the model. This can help you

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

X = np.random.rand(1000,5)
Y = np.random.rand(1000,1)

def build_model():
    model = Sequential()
    model.add(Dense(64,input_shape=(X.shape[1],)))
    model.add(Dense(Y.shape[1]))
    model.compile(loss='mse',optimizer='Adam')
    return model

epoch=2
batch_sizes = [128,256]
for i in range(len(batch_sizes)):
    model = build_model()
    history = model.fit(X, Y, batch_sizes[i], epochs=epoch, verbose=2)
    model.save('Model_' + str(batch_sizes[i]) + '.h5')

Then, the output looks like:

Epoch 1/2
8/8 - 0s - loss: 0.3164
Epoch 2/2
8/8 - 0s - loss: 0.1367
Epoch 1/2
4/4 - 0s - loss: 0.7221
Epoch 2/2
4/4 - 0s - loss: 0.4787
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