I like Machine Learning / Deep Learning.

6 minute read

Using TFReocrd File




  • We’ve learned how to make TFrecord file format by previous post.

    Making TFRecord File Format

  • At this post, we’ll make a model that classifies images with the TFRecord dataset made before.

  • TFRecord works with best performance when cooperated with Tensorflow. So, I will try to make all codes using the functions provided by Tensorflow as possible.







0. Prepare

  • Let’s load essential modules
import tensorflow as tf
from tqdm import tqdm
from sklearn.model_selection import train_test_split




  • Define the size of batch & prefetch in advance.
class CFG:
    BATCH_SIZE = 32
    BUFFER_SIZE = 500




  • Getring the full path of TFRecord files to apply to the map later.
Cat_Fearue_File_List = tf.io.gfile.listdir("./PetImages/Cat/TFRecord")
Cat_Fearue_File_List = list(map(lambda x:"./PetImages/Cat/TFRecord/" + x, Cat_Fearue_File_List))

print(len(Cat_Fearue_File_List))
Cat_Fearue_File_List[:10]

12427

['./PetImages/Cat/TFRecord/0.tfrecord',
 './PetImages/Cat/TFRecord/1.tfrecord',
 './PetImages/Cat/TFRecord/10.tfrecord',
 './PetImages/Cat/TFRecord/100.tfrecord',
 './PetImages/Cat/TFRecord/1000.tfrecord',
 './PetImages/Cat/TFRecord/10000.tfrecord',
 './PetImages/Cat/TFRecord/10001.tfrecord',
 './PetImages/Cat/TFRecord/10002.tfrecord',
 './PetImages/Cat/TFRecord/10003.tfrecord',
 './PetImages/Cat/TFRecord/10004.tfrecord']





Dog_Fearue_File_List = tf.io.gfile.listdir("./PetImages/Dog/TFRecord")
Dog_Fearue_File_List = list(map(lambda x:"./PetImages/Dog/TFRecord/" + x, Dog_Fearue_File_List))

print(len(Dog_Fearue_File_List))
Dog_Fearue_File_List[:10]

12397

['./PetImages/Dog/TFRecord/0.tfrecord',
 './PetImages/Dog/TFRecord/1.tfrecord',
 './PetImages/Dog/TFRecord/10.tfrecord',
 './PetImages/Dog/TFRecord/100.tfrecord',
 './PetImages/Dog/TFRecord/1000.tfrecord',
 './PetImages/Dog/TFRecord/10000.tfrecord',
 './PetImages/Dog/TFRecord/10001.tfrecord',
 './PetImages/Dog/TFRecord/10002.tfrecord',
 './PetImages/Dog/TFRecord/10003.tfrecord',
 './PetImages/Dog/TFRecord/10004.tfrecord']








1. Train & Validation Set Split

  • Let’s separate the Train / Val data set to be used for train. Divide by 8:2 Cat / Dog. dataset.



Cat_Train_File_List, Cat_Val_File_List = train_test_split(Cat_Fearue_File_List, test_size=0.2, random_state=123)

print("Cat Train : ",len(Cat_Train_File_List) , "Cat Val. : ",len(Cat_Val_File_List))

Cat Train :  9941 Cat Val. :  2486





Dog_Train_File_List, Dog_Val_File_List = train_test_split(Dog_Fearue_File_List, test_size=0.2, random_state=123)

print("Dog Train : ",len(Dog_Train_File_List) , "Dog Val. : ",len(Dog_Val_File_List))

Dog Train :  9917 Dog Val. :  2480




  • Merge the file list of train & val. for Cat / Dog.
Train_Feature_File_List = Cat_Train_File_List + Dog_Train_File_List

Val_Feature_File_List = Cat_Val_File_List + Dog_Val_File_List

print(len(Train_Feature_File_List) , len(Val_Feature_File_List) )

19858 4966


  • Shuffle !
Train_Feature_File_List = tf.random.shuffle(Train_Feature_File_List)

Train_Feature_File_List



<tf.Tensor: shape=(19858,), dtype=string, numpy=
array([b'./PetImages/Dog/TFRecord/10425.tfrecord',
       b'./PetImages/Cat/TFRecord/3119.tfrecord',
       b'./PetImages/Cat/TFRecord/5364.tfrecord', ...,
       b'./PetImages/Dog/TFRecord/9267.tfrecord',
       b'./PetImages/Dog/TFRecord/12042.tfrecord',
       b'./PetImages/Dog/TFRecord/551.tfrecord'], dtype=object)>

Val_Feature_File_List = tf.random.shuffle(Val_Feature_File_List)

Val_Feature_File_List

<tf.Tensor: shape=(4966,), dtype=string, numpy=
array([b'./PetImages/Dog/TFRecord/4587.tfrecord',
       b'./PetImages/Cat/TFRecord/387.tfrecord',
       b'./PetImages/Cat/TFRecord/5345.tfrecord', ...,
       b'./PetImages/Dog/TFRecord/2247.tfrecord',
       b'./PetImages/Dog/TFRecord/12447.tfrecord',
       b'./PetImages/Cat/TFRecord/9419.tfrecord'], dtype=object)>








2. Making Dataset

  • Now, let’s makes a datasets by reading TFRecord files

  • The overall sequence will be
    • Making datasets with full paths of TFRecord
    • Reading TFRecord file and make & apply .map function.
    • Apply shuffle / batch / prefetch to datasets in order.
    • Use datasets in train
  • Let’s check one by one.




2.1. Map Function

  • This function decodes the contents of TFRecord files


  • def map_fn(serialized_example):
    • The parameter(‘serialized_example’) passed over is the full path of TFRecord file.
    • The ‘serialized_example’ is passed to ‘map_fn’ when datasets are made by TFRecordDataset below.


  • feature = { ‘Feature’: tf.io.FixedLenFeature([49*1280], tf.float32), ‘Label’: tf.io.FixedLenFeature([1], tf.int64) }

    • This part defines the structure of TFRecord files we will read.
    • The data type of ‘Feature’ is float and the length is 62720, The data type of ‘Label’ is Int and the length is 1.
    • From above fact, we can easily notice with a little thought that we must know the structure of TFRecord files we use.
    • That’s is to say, when we distribute TFRecord files that we generated, we also have to let them know the structure of the TFRecord files.


  • example = tf.io.parse_single_example(serialized_example, feature)
    • It reads the existing TFRecord files from disc and decodes them


  • example[‘Feature’] = tf.reshape( example[‘Feature’] , (7,7,1280) )
    • As we flattened the data when we made TFRecord files, we have to restore(reshape) the original shape.


  • tf.squeeze( tf.one_hot(example[‘Label’] , depth=2) )
    • It generates lable info. Cat is 0 and Dog is 1 to One-Hot format.



def map_fn(serialized_example):

    feature = {
        'Feature': tf.io.FixedLenFeature([49*1280], tf.float32),
        'Label': tf.io.FixedLenFeature([1], tf.int64)
    }
    
    example = tf.io.parse_single_example(serialized_example, feature)
    
    example['Feature'] = tf.reshape( example['Feature'] , (7,7,1280) )
    
    return example['Feature'], tf.squeeze( tf.one_hot(example['Label'] , depth=2) )




2.2. Define Dataset

  • We use tf.data.TFRecordDataset function for making Dataset because we already have TFRecord file list
Train_Dataset = tf.data.TFRecordDataset( Train_Feature_File_List )
Val_Dataset = tf.data.TFRecordDataset( Val_Feature_File_List )

Train_Dataset

<TFRecordDatasetV2 shapes: (), types: tf.string>

Val_Dataset

<TFRecordDatasetV2 shapes: (), types: tf.string>




  • We finished making Dataset & funtions to apply Dataset, now we’ll apply shuffle / batch / prefetch.
Train_Dataset = Train_Dataset.map(map_fn , 
                      num_parallel_calls=tf.data.experimental.AUTOTUNE)

Train_Dataset = Train_Dataset.shuffle(CFG.BUFFER_SIZE).batch(CFG.BATCH_SIZE)
Train_Dataset = Train_Dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)

Val_Dataset = Val_Dataset.map(map_fn , 
                      num_parallel_calls=tf.data.experimental.AUTOTUNE)

Val_Dataset = Val_Dataset.shuffle(CFG.BUFFER_SIZE).batch(CFG.BATCH_SIZE)
Val_Dataset = Val_Dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)




  • Let’s check it works correctly.
for batch in Train_Dataset.take(1):
    batch

print(batch)
batch[1].numpy()

(<tf.Tensor: shape=(32, 7, 7, 1280), dtype=float32, numpy=
array([[[[-1.30283579e-01, -1.79280803e-01, -2.73918390e-01, ...,
          -2.35235289e-01, -2.51564652e-01, -1.24651089e-01],
         [-2.23974511e-01, -2.69972891e-01, -2.78214544e-01, ...,
          -1.61265686e-01, -2.63588488e-01,  4.82403219e-01],
         [-9.15986970e-02, -7.91421384e-02, -2.09058121e-01, ...,
          -2.46901169e-01, -2.35181734e-01, -2.75620580e-01],
         ...,
         [-4.28934097e-02, -3.80189456e-02, -9.37430859e-02, ...,
          -2.70000398e-01, -2.56699502e-01,  3.71812642e-01],
         [-3.37041589e-03,  2.37213349e+00, -2.73799729e-02, ...,
          -2.78330237e-01, -2.63371170e-01, -2.72408187e-01],
         [-2.69764900e-01, -9.48898420e-02, -6.93319291e-02, ...,
          -2.59258837e-01, -2.78016418e-01, -2.23277569e-01]],

        [[-2.40731761e-01, -1.49252594e-01, -1.29014462e-01, ...,
          -3.48842107e-02, -2.78116941e-01,  1.21828353e+00],
         [-2.78438926e-01,  5.36722839e-01, -1.36655658e-01, ...,
          -6.23840746e-03, -2.26522133e-01,  1.85954738e+00],
         [-6.25507459e-02, -1.00828111e-01, -1.55045748e-01, ...,
          -4.72838171e-02, -1.98967606e-01, -2.27459908e-01],
         ...,
         [-4.31086839e-04,  2.16324544e+00, -1.06345741e-02, ...,
          -3.63434367e-02, -3.13317887e-02,  9.24772203e-01],
         [-1.53233509e-06,  1.35951967e+01, -7.76352419e-04, ...,
          -4.41170437e-03, -2.31151477e-01, -2.63853192e-01],
         [-6.46062661e-03,  4.92377949e+00, -1.39266048e-02, ...,
          -5.87231778e-02,  1.09022892e+00, -2.71575540e-01]],

        [[-1.42593503e-01,  3.51380378e-01, -2.29087830e-01, ...,
          -9.77664907e-03, -2.43833497e-01,  6.59770250e-01],
         [-6.03562184e-02,  2.67279983e+00, -8.22568163e-02, ...,
          -3.87481693e-03, -3.45014818e-02,  2.45907426e+00],
         [-6.38377480e-03,  4.15273046e+00, -1.50325801e-02, ...,
          -8.94669294e-02, -1.27345668e-02, -2.78422236e-01],
         ...,
         [-3.85519373e-03, -1.10767812e-01, -3.39450780e-03, ...,
          -1.03545524e-02, -8.26057419e-03,  1.70724523e+00],
         [-5.78504521e-04,  6.60518837e+00, -2.84841983e-04, ...,
          -3.46982223e-03, -2.71046907e-02, -9.67810899e-02],
         [-5.40567897e-02, -2.77622372e-01, -3.78229097e-02, ...,
          -6.76211938e-02, -2.77685404e-01, -2.49596104e-01]],

        ...,


        [[ 2.25460219e+00, -2.29545057e-01,  7.19489276e-01, ...,
          -1.46996761e-02, -2.56974429e-01,  2.10201070e-01],
         [ 9.66896489e-02, -2.15796664e-01, -2.50947535e-01, ...,
          -2.59889185e-01, -2.60895252e-01,  1.05790734e-01],
         [-2.78464437e-01, -2.75186807e-01, -2.66715109e-01, ...,
          -2.64783144e-01, -1.05178565e-01,  4.61720973e-01],
         ...,
         [ 1.15485799e+00, -1.55356184e-01, -1.62961870e-01, ...,
          -7.94149265e-02, -2.78311014e-01,  7.28313923e-01],
         [ 2.17598176e+00, -8.69607255e-02,  2.29728460e-01, ...,
          -1.48992896e-01,  2.41546303e-01,  4.84176666e-01],
         [ 1.63772357e+00, -4.06442173e-02,  1.13258696e+00, ...,
          -2.36238405e-01, -2.71336257e-01,  6.56960234e-02]]]],
      dtype=float32)>, <tf.Tensor: shape=(32, 2), dtype=float32, numpy=
array([[0., 1.],
       [0., 1.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [0., 1.],
       [0., 1.],
       [0., 1.],
       [0., 1.],
       [0., 1.],
       [1., 0.],
       [0., 1.],
       [0., 1.],
       [1., 0.],
       [0., 1.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [0., 1.],
       [1., 0.],
       [0., 1.],
       [0., 1.],
       [1., 0.],
       [0., 1.],
       [1., 0.],
       [1., 0.],
       [0., 1.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.]], dtype=float32)>)





array([[0., 1.],
       [0., 1.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [0., 1.],
       [0., 1.],
       [0., 1.],
       [0., 1.],
       [0., 1.],
       [1., 0.],
       [0., 1.],
       [0., 1.],
       [1., 0.],
       [0., 1.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [0., 1.],
       [1., 0.],
       [0., 1.],
       [0., 1.],
       [1., 0.],
       [0., 1.],
       [1., 0.],
       [1., 0.],
       [0., 1.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.]], dtype=float32)



  • We now know that it reads as batch size even .take(1) and from the contents, it works correctly.








3. Model Define

  • We are going to make a simple dense net to classify the features from EfficientNet.

  • Pay attention to the input shape and make one suitable.

model = tf.keras.Sequential()

model.add( tf.keras.layers.InputLayer(input_shape=(7,7,1280)) )
model.add( tf.keras.layers.GlobalAveragePooling2D() )

model.add( tf.keras.layers.BatchNormalization() )
model.add( tf.keras.layers.Dropout(0.25) )
model.add( tf.keras.layers.Dense(256 , activation='relu') )

model.add( tf.keras.layers.BatchNormalization() )
model.add( tf.keras.layers.Dropout(0.25) )
model.add( tf.keras.layers.Dense(64 , activation='relu') )

model.add( tf.keras.layers.BatchNormalization() )
model.add( tf.keras.layers.Dropout(0.25) )
model.add( tf.keras.layers.Dense(2 , activation='softmax') )

model.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d (Gl (None, 1280)              0         
_________________________________________________________________
batch_normalization (BatchNo (None, 1280)              5120      
_________________________________________________________________
dropout (Dropout)            (None, 1280)              0         
_________________________________________________________________
dense (Dense)                (None, 256)               327936    
_________________________________________________________________
batch_normalization_1 (Batch (None, 256)               1024      
_________________________________________________________________
dropout_1 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 64)                16448     
_________________________________________________________________
batch_normalization_2 (Batch (None, 64)                256       
_________________________________________________________________
dropout_2 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 2)                 130       
=================================================================
Total params: 350,914
Trainable params: 347,714
Non-trainable params: 3,200
_________________________________________________________________




  • Choosing optimizer, configure it. Compile & Start training
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-2)

model.compile(optimizer=optimizer, 
              loss="categorical_crossentropy", metrics=["accuracy"]
             )

EPOCHS = 5

hist = model.fit(Train_Dataset, 
                 epochs=EPOCHS, 
                 validation_data=Val_Dataset, 
                 verbose=1)

Epoch 1/5
621/621 [==============================] - 285s 458ms/step - loss: 0.0579 - accuracy: 0.9812 - val_loss: 0.0319 - val_accuracy: 0.9887
Epoch 2/5
621/621 [==============================] - 362s 582ms/step - loss: 0.0383 - accuracy: 0.9867 - val_loss: 0.0371 - val_accuracy: 0.9881
Epoch 3/5
621/621 [==============================] - 384s 618ms/step - loss: 0.0339 - accuracy: 0.9892 - val_loss: 0.0327 - val_accuracy: 0.9895
Epoch 4/5
621/621 [==============================] - 473s 761ms/step - loss: 0.0355 - accuracy: 0.9880 - val_loss: 0.0291 - val_accuracy: 0.9899
Epoch 5/5
621/621 [==============================] - 436s 702ms/step - loss: 0.0331 - accuracy: 0.9902 - val_loss: 0.0357 - val_accuracy: 0.9895




  • It already shows high accuracy both of Train/Val set on the first epoch.
  • It works properly.




import matplotlib.pyplot as plt

plt.plot(hist.history["accuracy"])
plt.plot(hist.history["val_accuracy"])
plt.title("model accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.legend(["train", "validation"], loc="upper left")
plt.show()








4. Summary

  • We’ve learned how to read TFReocrd file format and apply to model in this post.

  • I think it’s important to know the structure of TFRecord file before reading & applying to train models.

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