layers.GRU(200, input_shape=[32, 32 * 3]),
layers.Dense(100, activation='relu'),
layers.Dense(10, activation='softmax')
])
model.summary()
# Compile the model
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# Train the model
history = model.fit(x_train, y_train, epochs=30, validation_split=0.1)
# Plot the learning curve
plot_utils.plot_learning_curve(training_acc=history.history['accuracy'],
validation_acc=history.history['val_accuracy'],
file_name='gru_learning_curve')
# Evaluate the model on the test set
results = model.evaluate(x_test, y_test)
print(f'Test accuracy: {np.round(results[1], 4)}')
# Using the model to make predictions
y_pred = model.predict_classes(x_test[:5])
plot_utils.plot_predictions(x_test[:5],
class_names,
y_test[:5],
y_pred,
file_name='gru_predictions')
'Shirt',
'Sneaker',
'Bag',
'Ankle boot'
]
# preprocess data
# Scale to num between 0 and 1
test_images = test_images / 255.0
# Define the model
model = keras.models.load_model('fashion_model.h5')
# Review summary
model.summary()
# Test model
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)
# Let's make some predictions
predictions = model.predict(test_images)
print('Predictions', predictions[0])
print('First prediction (most likely):', class_names[test_labels[np.argmax(predictions[0])]])
# Plot the first 10 test images, their predicted label, and the true label
# Color correct predictions in blue, incorrect predictions in red
plot_utils.plot_predictions(10, predictions, test_labels, test_images)
# Accuracy
acc = accuracy_score(y_true, y_pred)
print 'Accuracy on test set:', acc
# Find labels in use
label_list = sorted(df.y_true.unique())
label_list = [LABELS[l] for l in label_list]
# Plot normalized confusion matrix
cnf_matrix = confusion_matrix(y_true, y_pred)
output_file = os.path.join(OUTPUT_DIR, 'confusion_matrix.png')
_ = plot_confusion_matrix(cnf_matrix,
output_file,
classes=label_list,
normalize=True,
title='Confusion matrix (accuracy=%.2f)' % acc)
print 'Plot saved:', output_file
# Classification report (F1 score, etc.)
clf_report = classification_report(y_true, y_pred, target_names=label_list)
output_file = os.path.join(OUTPUT_DIR, 'classification_report.png')
plot_classification_report(clf_report, output_file)
print 'Plot saved:', output_file
# Plot predictions
output_file = os.path.join(OUTPUT_DIR, 'predictions.html')
title = 'Predictions (accuracy=%s)' % acc
plot_predictions(t, X_values, y_true, y_pred, output_file, title)
print 'Plot saved:', output_file
if vote_window > 0:
y_pred = predictions_vote(y_pred, vote_window)
# Accuracy
acc = accuracy_score(y_true, y_pred)
print 'Accuracy on test set:', acc
label_list = sorted(df.y_true.unique())
# Plot normalized confusion matrix
cnf_matrix = confusion_matrix(y_true, y_pred)
output_file = os.path.join(output_dir, 'confusion_matrix.png')
_ = plot_confusion_matrix(cnf_matrix,
output_file,
classes=label_list,
normalize=True,
title='Confusion matrix (accuracy=%.2f)' % acc)
print 'Plot saved:', output_file
# Classification report (F1 score, etc.)
clf_report = classification_report(y_true, y_pred)
output_file = os.path.join(output_dir, 'classification_report.png')
plot_classification_report(clf_report, output_file)
print 'Plot saved:', output_file
# Plot predictions
output_file = os.path.join(output_dir, 'predictions.html')
title = 'Predictions (accuracy=%s)' % acc
plot_predictions(t, X_values, y_true, y_pred, output_file, title)
print 'Plot saved:', output_file