Datasets:
HenryAI
/
KerasCodeExamples.txt

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from tensorflow.keras import layers
img_augmentation = Sequential(
[
layers.RandomRotation(factor=0.15),
layers.RandomTranslation(height_factor=0.1, width_factor=0.1),
layers.RandomFlip(),
layers.RandomContrast(factor=0.1),
],
name=\"img_augmentation\",
)
This Sequential model object can be used both as a part of the model we later build, and as a function to preprocess data before feeding into the model. Using them as function makes it easy to visualize the augmented images. Here we plot 9 examples of augmentation result of a given figure.
for image, label in ds_train.take(1):
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
aug_img = img_augmentation(tf.expand_dims(image, axis=0))
plt.imshow(aug_img[0].numpy().astype(\"uint8\"))
plt.title(\"{}\".format(format_label(label)))
plt.axis(\"off\")
png
Prepare inputs
Once we verify the input data and augmentation are working correctly, we prepare dataset for training. The input data are resized to uniform IMG_SIZE. The labels are put into one-hot (a.k.a. categorical) encoding. The dataset is batched.
Note: prefetch and AUTOTUNE may in some situation improve performance, but depends on environment and the specific dataset used. See this guide for more information on data pipeline performance.
# One-hot / categorical encoding
def input_preprocess(image, label):
label = tf.one_hot(label, NUM_CLASSES)
return image, label
ds_train = ds_train.map(
input_preprocess, num_parallel_calls=tf.data.AUTOTUNE
)
ds_train = ds_train.batch(batch_size=batch_size, drop_remainder=True)
ds_train = ds_train.prefetch(tf.data.AUTOTUNE)
ds_test = ds_test.map(input_preprocess)
ds_test = ds_test.batch(batch_size=batch_size, drop_remainder=True)
Training a model from scratch
We build an EfficientNetB0 with 120 output classes, that is initialized from scratch:
Note: the accuracy will increase very slowly and may overfit.
from tensorflow.keras.applications import EfficientNetB0
with strategy.scope():
inputs = layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3))
x = img_augmentation(inputs)
outputs = EfficientNetB0(include_top=True, weights=None, classes=NUM_CLASSES)(x)
model = tf.keras.Model(inputs, outputs)
model.compile(
optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]
)
model.summary()
epochs = 40 # @param {type: \"slider\", min:10, max:100}
hist = model.fit(ds_train, epochs=epochs, validation_data=ds_test, verbose=2)
Model: \"functional_1\"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 224, 224, 3)] 0
_________________________________________________________________
img_augmentation (Sequential (None, 224, 224, 3) 0
_________________________________________________________________
efficientnetb0 (Functional) (None, 120) 4203291
=================================================================
Total params: 4,203,291
Trainable params: 4,161,268
Non-trainable params: 42,023
_________________________________________________________________
Epoch 1/40
187/187 - 66s - loss: 4.9221 - accuracy: 0.0119 - val_loss: 4.9835 - val_accuracy: 0.0104
Epoch 2/40
187/187 - 63s - loss: 4.5652 - accuracy: 0.0243 - val_loss: 5.1626 - val_accuracy: 0.0145
Epoch 3/40
187/187 - 63s - loss: 4.4179 - accuracy: 0.0337 - val_loss: 4.7597 - val_accuracy: 0.0237
Epoch 4/40
187/187 - 63s - loss: 4.2964 - accuracy: 0.0421 - val_loss: 4.4028 - val_accuracy: 0.0378
Epoch 5/40
187/187 - 63s - loss: 4.1951 - accuracy: 0.0540 - val_loss: 4.3048 - val_accuracy: 0.0443
Epoch 6/40
187/187 - 63s - loss: 4.1025 - accuracy: 0.0596 - val_loss: 4.1918 - val_accuracy: 0.0526
Epoch 7/40
187/187 - 63s - loss: 4.0157 - accuracy: 0.0728 - val_loss: 4.1482 - val_accuracy: 0.0591
Epoch 8/40
187/187 - 62s - loss: 3.9344 - accuracy: 0.0844 - val_loss: 4.1088 - val_accuracy: 0.0638
Epoch 9/40
187/187 - 63s - loss: 3.8529 - accuracy: 0.0951 - val_loss: 4.0692 - val_accuracy: 0.0770
Epoch 10/40
187/187 - 63s - loss: 3.7650 - accuracy: 0.1040 - val_loss: 4.1468 - val_accuracy: 0.0719
Epoch 11/40
187/187 - 63s - loss: 3.6858 - accuracy: 0.1185 - val_loss: 4.0484 - val_accuracy: 0.0913
Epoch 12/40
187/187 - 63s - loss: 3.5942 - accuracy: 0.1326 - val_loss: 3.8047 - val_accuracy: 0.1072