上接： tensorflow读取图片
概述
本文以航空图分类为案例，建立卷积神经网络模型
建立模型
# 建立模型
model=tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(64,(3,3),input_shape=(256,256,3),activation="relu"))
model.add(tf.keras.layers.Conv2D(64,(3,3),activation="relu"))
model.add(tf.keras.layers.MaxPooling2D())
model.add(tf.keras.layers.Conv2D(128,(3,3),activation="relu"))
model.add(tf.keras.layers.Conv2D(128,(3,3),activation="relu"))
model.add(tf.keras.layers.MaxPooling2D())
model.add(tf.keras.layers.Conv2D(256,(3,3),activation="relu"))
model.add(tf.keras.layers.Conv2D(256,(3,3),activation="relu"))
model.add(tf.keras.layers.MaxPooling2D())
model.add(tf.keras.layers.Conv2D(512,(3,3),activation="relu"))
model.add(tf.keras.layers.MaxPooling2D())
model.add(tf.keras.layers.Conv2D(512,(3,3),activation="relu"))
model.add(tf.keras.layers.MaxPooling2D())
model.add(tf.keras.layers.Conv2D(1024,(3,3),activation="relu"))
model.add(tf.keras.layers.GlobalAveragePooling2D())
model.add(tf.keras.layers.Dense(1024,activation="relu"))
model.add(tf.keras.layers.Dense(256,activation="relu"))
model.add(tf.keras.layers.Dense(1,activation="sigmoid"))
model.summary()
训练
model.compile(optimizer="adam",loss="binary_crossentropy",metrics=["acc"])
history=model.fit(train_data2,epochs=15,validation_data=test_data2)
查看
plt.plot(history.epoch,history.history.get("acc"),label="acc")
plt.plot(history.epoch,history.history.get("val_acc"),label="val_acc")
# 必须加此代码，否则不显示图例
plt.legend()
plt.show()
plt.plot(history.epoch,history.history.get("loss"),label="loss")
plt.plot(history.epoch,history.history.get("val_loss"),label="val_loss")
# 必须加此代码，否则不显示图例
plt.legend()
plt.show()