Модель Keras дает одно и то же значение прогноза для всех входных данных, хотя она имеет высокую точность обучения. ⇐ Python

[Anonymous]

Код: Выделить всё

import numpy as np
import tensorflow as tf
import keras
from keras import layers
from tensorflow import data as tf_data
import matplotlib.pyplot as plt

# Define image size and batch size
image_size = (180, 180)
batch_size = 32

# Load the dataset
train_ds, val_ds = keras.utils.image_dataset_from_directory(
"images",
validation_split=0.2,
subset="both",
seed=1337,
image_size=image_size,
batch_size=batch_size,
)

# Define data augmentation layers
data_augmentation_layers = [
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.1),
]

# Data augmentation function
def data_augmentation(images):
for layer in data_augmentation_layers:
images = layer(images)
return images

# Apply data augmentation to the dataset
augmented_train_ds = train_ds.map(
lambda x, y: (data_augmentation(x), y))

# Apply data augmentation to the training images
train_ds = train_ds.map(
lambda img, label: (data_augmentation(img), label),
num_parallel_calls=tf_data.AUTOTUNE,
)

# Prefetch data for faster processing
train_ds = train_ds.prefetch(tf_data.AUTOTUNE)
val_ds = val_ds.prefetch(tf_data.AUTOTUNE)

# Function to create the model with a simplified architecture
def make_simplified_model(input_shape, num_classes):
inputs = keras.Input(shape=input_shape)

# Entry block
x = layers.Rescaling(1.0 / 255)(inputs)  # Rescale the input images to [0, 1]
x = layers.Conv2D(128, 3, strides=2, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)

# First block (simplified)
x = layers.SeparableConv2D(256, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
x = layers.MaxPooling2D(3, strides=2, padding="same")(x)

# Second block (simplified)
x = layers.SeparableConv2D(512, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
x = layers.MaxPooling2D(3, strides=2, padding="same")(x)

# Global average pooling and dropout
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dropout(0.25)(x)

# Output layer (binary classification)
outputs = layers.Dense(1, activation=tf.nn.sigmoid)(x)

return keras.Model(inputs, outputs)

# Build the model with the simplified architecture
model = make_simplified_model(input_shape=image_size + (3,), num_classes=2)

# Visualize the model structure
keras.utils.plot_model(model, show_shapes=True)

# Number of epochs for training
epochs = 25

# Define callbacks (to save the model during training)
callbacks = [
keras.callbacks.ModelCheckpoint("save_at_{epoch}.keras"),
]

# Compile the model
model.compile(
optimizer=keras.optimizers.Adam(3e-4),
loss=keras.losses.BinaryCrossentropy(from_logits=False),
metrics=[keras.metrics.BinaryAccuracy(name="acc")],
)

# Train the model
history = model.fit(
train_ds,
epochs=epochs,
callbacks=callbacks,
validation_data=val_ds,
)

# Plot training and validation accuracy and loss
plt.plot(history.history['acc'], label='Training Accuracy')
plt.plot(history.history['val_acc'], label='Validation Accuracy')
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.legend()
plt.show()

Вот как я тренирую свою модель, а следующий код проверяет прогноз:

Код: Выделить всё

import tensorflow as tf
from tensorflow import keras
import numpy as np
from PIL import Image

# Load the saved model with weights (make sure you use the correct epoch file)
model = keras.models.load_model("save_at_1.keras", compile=False)  # Adjust the filename to the correct one if needed

# Test the model with a sample image
sample_image_path = "sample_image.jpg"   # Replace with your image file path

def preprocess_image(image_path, target_size):
# Load and preprocess the image
img = Image.open(image_path)
img = img.resize(target_size)  # Resize to model's expected input size
img_array = np.array(img).astype("float32") / 255.0  # Normalize
img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
return img_array

# Preprocess the sample image
input_image = preprocess_image(sample_image_path, target_size=(180, 180))

# Run prediction
prediction = model.predict_on_batch(input_image)
print("Prediction:", prediction[0][0])

# Interpret the result
if prediction[0][0] > 0.5:
print("Prediction: Class 1 (e.g., Open Eyes)")
else:
print("Prediction: Class 0 (e.g., Closed Eyes)")

Выходной прогноз всегда имеет одно и то же значение. Я пытался найти решение часами, но просто застрял и надеюсь, что кто-нибудь поможет мне с этим!!!
Пытался удалить несколько слоев
Пытался уменьшить размер пакета
Пробовал разные изображения
Пробывал разные эпохи
Пытался преобразовать модель в другой тип данных, но результат тот же

Подробнее здесь: https://stackoverflow.com/questions/791 ... ugh-it-has