CODEWITHSUNDEEP
pythontensorflowkerasdeep-learning
johni07
johni07

Reputation: 771

Tensorflow Keras use tfrecords also for validation

Right now I'm using keras with tensorflow backend. The dataset was stored in the tfrecords format. Training without any validation set is working, but how to integrate my validation-tfrecords also?

Lets assume this code as coarse skeleton:

def _ds_parser(proto):
    features = {
        'X': tf.FixedLenFeature([], tf.string),
        'Y': tf.FixedLenFeature([], tf.string)
    }

    parsed_features = tf.parse_single_example(proto, features)

    # get the data back as float32
    parsed_features['X'] = tf.decode_raw(parsed_features['I'], tf.float32)
    parsed_features['Y'] = tf.decode_raw(parsed_features['Y'], tf.float32)

    return parsed_features['X'],  parsed_features['Y']

def datasetLoader(dataSetPath, batchSize):
    dataset = tf.data.TFRecordDataset(dataSetPath)

    # Maps the parser on every filepath in the array. You can set the number of parallel loaders here
    dataset = dataset.map(_ds_parser, num_parallel_calls=8)

    # This dataset will go on forever
    dataset = dataset.repeat()

    # Set the batchsize
    dataset = dataset.batch(batchSize)

    # Create an iterator
    iterator = dataset.make_one_shot_iterator()

    # Create your tf representation of the iterator
    X, Y = iterator.get_next()  

    # Bring the date back in shape
    X = tf.reshape(I, [-1, 66, 198, 3])
    Y = tf.reshape(Y,[-1,1])    

    return X, Y

X, Y = datasetLoader('PATH-TO-DATASET', 264)

model_X = keras.layers.Input(tensor=X)

model_output = keras.layers.Conv2D(filters=16, kernel_size=3, strides=1, padding='valid', activation='relu',
                                           input_shape=(-1, 66, 198, 3))(model_X)
model_output = keras.layers.Dense(units=1, activation='linear')(model_output)

model = keras.models.Model(inputs=model_X, outputs=model_output)

model.compile(
    optimizer=optimizer,
    loss='mean_squared_error',
    target_tensors=[Y]
)

parallel_model.fit(
    epochs=epochs,
    steps_per_epoch=stepPerEpoch,
    shuffle=False,
    validation_data=????
)

The question is, how to pass the validation set?

I have found something related here: gcloud-ml-engine-with-keras, but I'm not sure how to fit this into my problem.

Upvotes: 4

Views: 2114

Answers (2)

johni07
johni07

Reputation: 771

Ok I found the answer myself: basically it's done by simply change import keras toimport tensorflow.keras as keras. Tf.keras allows you to pass the validation set also as tensor:

X, Y = datasetLoader('PATH-TO-DATASET', 264)
X_val, Y_val = datasetLoader('PATH-TO-VALIDATION-DATASET', 264)

# ... define and compile the model like above

parallel_model.fit(
    epochs= epochs,
    steps_per_epoch= STEPS_PER_EPOCH,
    shuffle= False,
    validation_data= (X_val, Y_val),
    validation_steps= STEPS_PER_VALIDATION_EPOCH
)

Upvotes: 3

Sharky
Sharky

Reputation: 4533

First, You don't need to use iterator. Keras model will accept dataset object instead separate data/labels parameters, and will handle iteration. You only need to specify steps_per_epoch, hence you need to know dataset size. If you have separate tfrecords file for train/validation, then you can just create dataset object and pass it to validation_data. If you have one file and you'd like to split it, you can do

dataset = tf.data.TFRecordDataset('file.tfrecords')
dataset_train = dataset.take(size)
dataset_val = dataset.skip(size)

...

Upvotes: 5

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