python keras构建和训练模型简便性初探

import numpy as np
import keras
import matplotlib.pyplot as plt
# ---- Load data ----
(train_images, train_labels), (test_images, test_labels) = keras.datasets.mnist.load_data()
# Preprocess data (normalize)
train_images = train_images / 255.0
test_images = test_images / 255.0
# ---- Build the model ----
model = keras.Sequential([
    # Input: Transforms images into 1D vectors
    keras.layers.Flatten(input_shape=(28, 28)),
    # Hidden: 128 neurons for feature learning
    keras.layers.Dense(128, activation='relu'),
    # Output: Probabilities for each class
    keras.layers.Dense(10, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# ---- Train ----
history = model.fit(train_images, train_labels, epochs=10)
# ---- Evaluate ----
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test Accuracy:', test_acc)
# ---- Visualize ----
# Plot training & validation accuracy history
plt.plot(history.history['accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train'], loc='upper left')
plt.show()