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cnn_normal_binary.py
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cnn_normal_binary.py
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import tensorflow
from keras.callbacks import EarlyStopping
import pathlib
import matplotlib.pyplot as plt
data_dir = pathlib.Path("AnnotatedImages2")
batch_size = 32
img_height = 245
img_width = 262
train_ds = tensorflow.keras.utils.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tensorflow.keras.utils.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
class_names = train_ds.class_names
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=tensorflow.data.AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=tensorflow.data.AUTOTUNE)
model = tensorflow.keras.Sequential(
[
tensorflow.keras.layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
tensorflow.keras.layers.Conv2D(8, (3,3), padding='same', activation="relu"),
tensorflow.keras.layers.MaxPooling2D((3, 3), strides=3),
tensorflow.keras.layers.Conv2D(16, (3,3), padding='same', activation="relu"),
tensorflow.keras.layers.MaxPooling2D((3, 3), strides=3),
tensorflow.keras.layers.Conv2D(64, (3,3), padding='same', activation="relu"),
tensorflow.keras.layers.MaxPooling2D((2, 2), strides=2),
tensorflow.keras.layers.Flatten(),
tensorflow.keras.layers.Dense(300, activation="relu"),
tensorflow.keras.layers.Dropout(0.25),
tensorflow.keras.layers.Dense(len(class_names), activation="softmax")
]
)
tensorflow.keras.utils.plot_model(model, "model.png", show_shapes=True)
model.compile(optimizer='adam', loss=tensorflow.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['accuracy'])
model.summary()
model_checkpoint_callback = tensorflow.keras.callbacks.ModelCheckpoint(
filepath='model.h5',
save_weights_only=False,
monitor='val_loss',
mode='min',
save_best_only=True)
callbacksE = [
EarlyStopping(patience=4, restore_best_weights=True),
model_checkpoint_callback,
]
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=100,
callbacks=callbacksE,
)
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(100)
plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.savefig("rgb_binary.png")