浣跨敤Keras搴撳彲浠ュ湪Python涓婃瀯寤哄拰璁粌娣卞害瀛︿範妯″瀷銆備互涓嬫槸浣跨敤Keras鐨勫熀鏈楠わ細

1. 瀹夎Keras搴擄細浣跨敤pip鍛戒护瀹夎Keras搴撱€傚湪缁堢鎴栧懡浠ゆ彁绀虹涓繍琛屼互涓嬪懡浠わ細pip install keras

2. 瀵煎叆Keras搴擄細鍦≒ython鑴氭湰涓鍏eras搴擄紝浣跨敤浠ヤ笅浠ｇ爜锛?code>import keras

3. 鏋勫缓妯″瀷锛氫娇鐢↘eras鐨?code>Sequential妯″瀷绫诲彲浠ユ瀯寤轰竴涓『搴忔ā鍨嬶紝鍗冲眰鎸夐『搴忓爢鍙犵殑妯″瀷銆備緥濡傦紝鍙互浣跨敤浠ヤ笅浠ｇ爜鍒涘缓涓€涓畝鍗曠殑绁炵粡缃戠粶妯″瀷锛?/p>

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))

4. 缂栬瘧妯″瀷锛氬湪璁粌妯″瀷涔嬪墠锛岄渶瑕佷娇鐢?code>compile鏂规硶鏉ラ厤缃ā鍨嬬殑瀛︿範杩囩▼銆備緥濡傦紝鍙互浣跨敤浠ヤ笅浠ｇ爜缂栬瘧涓婅堪妯″瀷锛?/li>

model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])

5. 璁粌妯″瀷锛氫娇鐢?code>fit鏂规硶鏉ヨ缁冩ā鍨嬶紝鍗冲皢杈撳叆鏁版嵁鍜屽搴旂殑鏍囩浼犻€掔粰妯″瀷锛岀劧鍚庤繘琛屽弽鍚戜紶鎾拰鍙傛暟鏇存柊銆備緥濡傦紝鍙互浣跨敤浠ヤ笅浠ｇ爜璁粌妯″瀷锛?/li>

model.fit(x_train, y_train, epochs=10, batch_size=32)

6. 璇勪及妯″瀷锛氫娇鐢?code>evaluate鏂规硶鏉ヨ瘎浼版ā鍨嬬殑鎬ц兘銆備緥濡傦紝鍙互浣跨敤浠ヤ笅浠ｇ爜璇勪及妯″瀷锛?/li>

loss_and_metrics = model.evaluate(x_test, y_test, batch_size=128)

7. 浣跨敤妯″瀷锛氳缁冨畬妯″瀷鍚庯紝鍙互浣跨敤predict鏂规硶鏉ヨ繘琛岄娴嬨€備緥濡傦紝鍙互浣跨敤浠ヤ笅浠ｇ爜瀵规柊鏍锋湰杩涜棰勬祴锛?/li>

classes = model.predict(x_new)

浠ヤ笂鏄娇鐢↘eras鏋勫缓鍜岃缁冩ā鍨嬬殑鍩烘湰姝ラ銆傛牴鎹叿浣撲换鍔＄殑涓嶅悓锛岃繕鍙互浣跨敤鏇村鐨凨eras鍔熻兘鍜屽眰鏉ユ瀯寤烘洿澶嶆潅鐨勬ā鍨嬨€?/p>