Hoher Testgenauigkeitswert, aber schlecht vorhersagen ⇐ Python

[Anonymous]

Code: Select all

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import MobileNet

batch_size = 8
num_epochs = 20
num_classes = 4
train_dir = '/Users/borakarakus/Desktop/dataset/Training'
test_dir = '/Users/borakarakus/Desktop/dataset/Testing'

train_datagen = ImageDataGenerator()
test_datagen = ImageDataGenerator()

train_data = train_datagen.flow_from_directory(
train_dir,
target_size=(300, 300),
batch_size=batch_size,
class_mode='categorical',
shuffle=False
)

test_data = test_datagen.flow_from_directory(
test_dir,
target_size=(300, 300),
batch_size=batch_size,
class_mode='categorical',
shuffle=False
)

# Create MobileNet model
model = MobileNet(input_shape=(300, 300, 3),
include_top=False,
weights='/Users/borakarakus/.keras/models/mobilenet_1_0_224_tf_no_top.h5')

# Freeze layers
for layer in model.layers:
layer.trainable = False

# Add new top layers
x = model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(128, activation='relu')(x)
x = tf.keras.layers.Dropout(0.2)(x)
predictions = tf.keras.layers.Dense(num_classes, activation='softmax')(x)

model = tf.keras.models.Model(inputs=model.input, outputs=predictions)

# Compile model
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])

# Train model
history = model.fit(train_data,
epochs=20)

# Evaluate model
score = model.evaluate(test_data)

# Print accuracy
print('Test accuracy:', score[1])

model.save('SA_HUS_MobileNetmodel_F1.h5')
< /code>
Mein Code gibt mir 0,90 Testgenauigkeitswert, aber andere Metriken sind so schlecht, bitte helfen Sie < /p>
Test accuracy: 0.909229576587677

Classification Report:
precision    recall  f1-score   support

0       0.24      0.27      0.25       300
1       0.25      0.19      0.22       306
2       0.34      0.36      0.35       405
3       0.22      0.24      0.23       300

accuracy                           0.27      1311
macro avg       0.26      0.26      0.26      1311
weighted avg       0.27      0.27      0.27      1311

Accuracy: 0.2707856598016781
Precision (Weighted): 0.27067005240444436``
Recall (Weighted): 0.2707856598016781
F1 Score (Weighted): 0.2693025334531395