I23_MLPin_training/img_classification_kerastuning.py at master · DiamondLightSource/I23_MLPin_training · GitHub

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from gc import callbacks
from sklearn import preprocessing
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Dropout
import matplotlib.pyplot as plt
import os
import scikeras as sc


print("Using TensorFlow v%s" % tf.__version__)
acc_str = "accuracy" if tf.__version__[:2] == "2." else "acc"

#data_dir = pathlib.Path("C:/Users/ULTMT/Documents/code/TFOD/I23_MLPin_training/goniopin/cropped")
cwd = os.getcwd()
data_dir = os.path.join(cwd, "goniopin", "cropped")
batch_size = 10
img_height = 250 #250 #964
img_width = 160 #160 #1292
image_size = (img_height, img_width)
seed = 28273492

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="training",
    seed=seed,
    image_size=(img_height, img_width),
    batch_size=batch_size,
)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="validation",
    seed=seed,
    image_size=(img_height, img_width),
    batch_size=batch_size,
)

plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
    for i in range(9):
        ax = plt.subplot(3, 3, i + 1)
        plt.imshow(images[i].numpy().astype("uint8"))
        plt.title(int(labels[i]))
        plt.axis("off")

plt.show()

normalization_layer = keras.layers.Rescaling(
    1.0 / 255
)

data_augmentation = Sequential(
    [
        keras.layers.RandomFlip(input_shape=(img_height, img_width, 3)),
        keras.layers.RandomRotation(45),
    ]
)

plt.figure(figsize=(10, 10))
for images, _ in train_ds.take(1):
    for i in range(9):
        augmented_images = data_augmentation(images)
        ax = plt.subplot(3, 3, i + 1)
        plt.imshow(augmented_images[0].numpy().astype("uint8"))
        plt.axis("off")
plt.show()

def make_model(input_shape, num_classes):
    inputs = keras.Input(shape=input_shape)
    x = data_augmentation(inputs)
    x = layers.Rescaling(1./255)(x)
    x = layers.Conv2D(32, 3, strides=2, padding="same")(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation("relu")(x)
    x = layers.Conv2D(64, 3, padding="same")(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation("relu")(x)
    previous_block_activation = x  # Set aside residual

    for size in [128, 256, 512, 728]:
        x = layers.Activation("relu")(x)
        x = layers.SeparableConv2D(size, 3, padding="same")(x)
        x = layers.BatchNormalization()(x)

        x = layers.Activation("relu")(x)
        x = layers.SeparableConv2D(size, 3, padding="same")(x)
        x = layers.BatchNormalization()(x)

        x = layers.MaxPooling2D(3, strides=2, padding="same")(x)

        # Project residual
        residual = layers.Conv2D(size, 1, strides=2, padding="same")(
            previous_block_activation
        )
        x = layers.add([x, residual])  # Add back residual
        previous_block_activation = x  # Set aside next residual

    x = layers.SeparableConv2D(1024, 3, padding="same")(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation("relu")(x)

    x = layers.GlobalAveragePooling2D()(x)
    if num_classes == 2:
        activation = "sigmoid"
        units = 1
    else:
        activation = "softmax"
        units = num_classes

    x = layers.Dropout(0.5)(x)
    outputs = layers.Dense(units, activation=activation)(x)
    return keras.Model(inputs, outputs)


model = make_model(input_shape=image_size + (3,), num_classes=2)

epochs = 50

callbacks = [
    keras.callbacks.ModelCheckpoint("save_at_{epoch}.h5"), tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3, restore_best_weights=True),
]
model.compile(
    optimizer=keras.optimizers.Adam(1e-3),
    loss="binary_crossentropy",
    metrics=["accuracy"],
)
model.fit(
    train_ds, epochs=epochs, callbacks=callbacks, validation_data=val_ds,
)

model.save("final.h5")