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csy3025-assignment-2
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Joshua Perry 2024-05-19 23:06:27 +01:00
parent 7b11c01c02
commit bbb20e3b3a
5 changed files with 104 additions and 21 deletions
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37
main.py
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@ -4,6 +4,7 @@ import tensorflow as tf
import os
#Import Data
PATH = os.path.join(os.getcwd(), "crop-data")
@ -23,12 +24,13 @@ validation_data = tf.keras.utils.image_dataset_from_directory(validation_data,
batch_size=BATCH_SIZE,
image_size=IMG_SIZE)
test_data = tf.keras.utils.image_dataset_from_directory(test_data,
shuffle=True,
batch_size=BATCH_SIZE,
image_size=IMG_SIZE)
train_size = len(training_data)-len(validation_data)
test_data = training_data.skip(train_size).take(len(validation_data))
class_names = training_data.class_names
training_data = training_data.take(train_size)
print(f"Train: {len(training_data)}\nValid: {len(validation_data)}\nTest: {len(test_data)}")
#View Data
plt.figure(figsize=(10,10))
@ -54,9 +56,6 @@ data_augmentation = tf.keras.Sequential([
tf.keras.layers.RandomRotation(0.2)
])
#Preprocessing
preprocess_input = tf.keras.applications.mobilenet_v3.preprocess_input
#Create Base Model From MobileNetV3
IMG_SHAPE = IMG_SIZE + (3,)
base_model = tf.keras.applications.MobileNetV3Large(
@ -85,7 +84,6 @@ predication_batch = prediction_layer(feature_batch_avg)
inputs = tf.keras.Input(shape=(160,160,3))
x = data_augmentation(inputs)
x = preprocess_input(x)
x = base_model(x, training=False)
x = global_avg_layer(x)
x = tf.keras.layers.Dropout(0.2)(x)
@ -102,17 +100,13 @@ base_learning_rate = 0.0001
training_data = training_data.map(lambda x,y: (x, tf.one_hot(y,38)))
validation_data = validation_data.map(lambda x,y: (x, tf.one_hot(y,38)))
test_data = test_data.map(lambda x,y: (x, tf.one_hot(y,38)))
optimizer = tf.keras.optimizers.Adam(learning_rate=base_learning_rate)
loss = tf.keras.losses.CategoricalCrossentropy()
metrics = [tf.keras.metrics.CategoricalAccuracy()]
phy_dev = tf.config.list_physical_devices("GPU")
for gpu in phy_dev:
tf.config.experimental.set_memory_growth(gpu, True)
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
@ -131,7 +125,7 @@ lr_schedule = tf.keras.callbacks.ReduceLROnPlateau(
min_lr=1e-6
)
earky_stopping = tf.keras.callbacks.EarlyStopping(
early_stopping = tf.keras.callbacks.EarlyStopping(
monitor="val_loss",
patience=10,
restore_best_weights=True
@ -140,6 +134,15 @@ earky_stopping = tf.keras.callbacks.EarlyStopping(
history = model.fit(training_data,
epochs=initial_epochs,
validation_data=validation_data,
callbacks=[lr_schedule])
callbacks=[lr_schedule, early_stopping])
model.save("crop-classifier-callbacks.keras")
model.save("crop-classifier-better-test.keras")
#Evaluate Model
results = model.evaluate(validation_data)
print(f"Validation Loss: {results[0]}")
print(f"Validation Accuracy: {results[1]}")
results = model.evaluate(test_data)
print(f"Test Loss: {results[0]}")
print(f"Test Accuracy: {results[1]}")

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results.py Normal file
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@ -0,0 +1,42 @@
import numpy as np
import tensorflow as tf
import os
#Import Data
PATH = os.path.join(os.getcwd(), "crop-data")
validation_data = os.path.join(PATH, "valid")
validation_data_32 = tf.keras.utils.image_dataset_from_directory(validation_data,
shuffle=True,
batch_size=32,
image_size=(160,160))
validation_data_64 = tf.keras.utils.image_dataset_from_directory(validation_data,
shuffle=True,
batch_size=64,
image_size=(160,160))
AUTOTUNE = tf.data.AUTOTUNE
validation_data_32 = validation_data_32.prefetch(buffer_size=AUTOTUNE)
validation_data_64 = validation_data_64.prefetch(buffer_size=AUTOTUNE)
validation_data_32 = validation_data_32.map(lambda x,y: (x, tf.one_hot(y,38)))
validation_data_64 = validation_data_64.map(lambda x,y: (x, tf.one_hot(y,38)))
model1 = tf.keras.models.load_model("crop-classifier.keras")
model2 = tf.keras.models.load_model("crop-classifier-64-batch.keras")
model3 = tf.keras.models.load_model("crop-classifier-callbacks.keras")
result = model1.evaluate(validation_data_32)
print(f"Validation Loss: {result[0]}")
print(f"Accuracy: {result[1]}")
result = model2.evaluate(validation_data_64)
print(f"Validation Loss: {result[0]}")
print(f"Accuracy: {result[1]}")
result = model3.evaluate(validation_data_64)
print(f"Validation Loss: {result[0]}")
print(f"Accuracy: {result[1]}")

46
test.py
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@ -1,4 +1,42 @@
#Load model
#Load test data
#Test
#Print results
import numpy as np
import tensorflow as tf
import os
#Import Data
PATH = os.path.join(os.getcwd(), "crop-data")
test_data = os.path.join(PATH, "test")
test_data_32 = tf.keras.utils.image_dataset_from_directory(test_data,
shuffle=True,
batch_size=32,
image_size=(160,160))
test_data_64 = tf.keras.utils.image_dataset_from_directory(test_data,
shuffle=True,
batch_size=64,
image_size=(160,160))
AUTOTUNE = tf.data.AUTOTUNE
test_data_32 = test_data_32.prefetch(buffer_size=AUTOTUNE)
test_data_64 = test_data_64.prefetch(buffer_size=AUTOTUNE)
test_data_32 = test_data_32.map(lambda x,y: (x, tf.one_hot(y,38)))
test_data_64 = test_data_64.map(lambda x,y: (x, tf.one_hot(y,38)))
model1 = tf.keras.models.load_model("crop-classifier.keras")
model2 = tf.keras.models.load_model("crop-classifier-64-batch.keras")
model3 = tf.keras.models.load_model("crop-classifier-callbacks.keras")
result = model1.evaluate(test_data_32)
print(f"Test Loss: {result[0]}")
print(f"Test Accuracy: {result[1]}")
result = model2.evaluate(test_data_64)
print(f"Test Loss: {result[0]}")
print(f"Test Accuracy: {result[1]}")
result = model3.evaluate(test_data_64)
print(f"Test Loss: {result[0]}")
print(f"Test Accuracy: {result[1]}")