CODEWITHSUNDEEP
pythontensorflowkerasdeep-learningneural-network
TDuncker
TDuncker

Reputation: 45

Unable to reset model when looping model.fit

Using Keras, I am trying to loop a training session 10 times with different splits of data. Though, after every loop my accuracy increases a lot probably because it doesn't reset and sees new data in new groups (data trained on might appear in test next loop)

I expected model.fit to reset it over, as pr. an answer here saying it does so but I can't get it to. I then tried K.clear_session() in the start of the loop, as pr. example 1 here, but it does nothing. I can save an untrained model the first time and reload it at start of loop, but this seems like a bad method/bad practice. What can I do instead/am I doing wrong?

from tensorflow.keras import backend as K

for i in range(0, 10):
    print("Starting loop " + str(i))
    K.clear_session()
    model = keras.Model(inputs=inputs, outputs=outputs, name="SchoolProject")
    model.compile(loss=tensorflow.keras.losses.binary_crossentropy, optimizer=tensorflow.keras.optimizers.Adam(lr=hpInitialLearningRate), metrics=['accuracy'])
    trainData, valData, testData, trainTruth, valTruth, testTruth = getTrainValAndTestSet()
    model.fit(trainData, trainTruth, epochs=hpEpochs, verbose=1, callbacks=callbacks_list, validation_data=(valData, valTruth))
    score = model.evaluate(testData, testTruth, verbose=1)
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])
    testAccList.append(score[1])
    print("Ending loop " + str(i))

Upvotes: 2

Views: 1772

Answers (1)

Nicolas Gervais
Nicolas Gervais

Reputation: 36664

The easiest way would be to define your model inside the loop. Here's an example. You'll see that every iteration, the accuracy starts at random before improving.

import tensorflow as tf

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255

for i in range(5):
    model = tf.keras.Sequential([
            tf.keras.Input(shape=(28, 28)),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(32, activation='relu'),
            tf.keras.layers.Dense(64, activation='relu'),
            tf.keras.layers.Dense(10, activation="softmax"),
        ])
    model.compile(loss="sparse_categorical_crossentropy", optimizer="adam",
                  metrics=["accuracy"])
    model.fit(x_train, y_train, batch_size=16, epochs=1, validation_split=0.1)

Resetting the weights manually is a little more complicated.

Upvotes: 4

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