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Configuration error
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from tensorflow.keras.utils import image_dataset_from_directory
from tensorflow.keras import Sequential
from tensorflow.keras.layers import (
Dense,
Conv2D,
MaxPooling2D,
Flatten,
BatchNormalization,
Dropout,
)
from utils import plot_accuracy, plot_loss, process
import numpy as np
from sklearn.metrics import classification_report
train_ds = image_dataset_from_directory(
directory="sports-classification/train",
labels="inferred",
label_mode="int",
batch_size=64,
image_size=(256, 256),
)
validation_ds = image_dataset_from_directory(
directory="sports-classification/valid",
labels="inferred",
label_mode="int",
batch_size=64,
image_size=(256, 256),
)
test_ds = image_dataset_from_directory(
directory="sports-classification/test",
labels="inferred",
label_mode="int",
batch_size=64,
image_size=(256, 256),
)
train_ds = train_ds.map(process)
validation_ds = validation_ds.map(process)
test_ds = test_ds.map(process)
model = Sequential()
model.add(
Conv2D(
128,
kernel_size=(3, 3),
padding="valid",
activation="leaky_relu",
input_shape=(256, 256, 3),
)
)
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding="valid"))
model.add(Conv2D(64, kernel_size=(3, 3), padding="valid", activation="leaky_relu"))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding="valid"))
model.add(Conv2D(32, kernel_size=(3, 3), padding="valid", activation="leaky_relu"))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding="valid"))
model.add(Flatten())
model.add(Dense(512, activation="leaky_relu"))
model.add(Dropout(0.1))
model.add(Dense(256, activation="leaky_relu"))
model.add(Dropout(0.1))
model.add(Dense(128, activation="leaky_relu"))
model.add(Dropout(0.1))
model.add(Dense(100, activation="softmax"))
model.compile(
optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
)
callback = tf.keras.callbacks.EarlyStopping(monitor="loss", patience=2)
history = model.fit(
train_ds,
epochs=50,
batch_size=32,
callbacks=[callback],
validation_data=validation_ds,
)
plot_loss(history)
plot_accuracy(history)
y_pred = np.array([])
y_true = np.array([])
for x, y in test_ds:
y_pred = np.concatenate([y_pred, model.predict_classes(x)])
y_true = np.concatenate([y_true, np.argmax(y.numpy(), axis=-1)])
print("Classification Report: \n", classification_report(y_pred, y_true)) |