Python Keras CNN模型预测的所有输入值相同，在训练期间不会提高准确性 我正努力跟随英伟达自驾车美国有线电视新闻网报纸。然而，当我运行代码时，我的精度在训练期间保持不变，损失非常小。该模型还预测了任何输入的相同值，非常接近于0。预期产出主要在-4和+4之间 import tensorflow.compat.v1 as tf import scipy.misc import random from tensorflow import keras from tensorflow.keras import datasets, layers, models import numpy as np model = models.Sequential() model.add(layers.Conv2D(24, (5, 5), strides=(2, 2), input_shape=(66, 200, 3))) model.add(layers.Activation('relu')) model.add(layers.Conv2D(36, (5, 5), strides=(2, 2))) model.add(layers.Activation('relu')) model.add(layers.Conv2D(48, (5, 5), strides=(2, 2))) model.add(layers.Activation('relu')) model.add(layers.Conv2D(64, (3, 3), strides=(1, 1))) model.add(layers.Activation('relu')) model.add(layers.Conv2D(64, (3, 3), strides=(1, 1))) model.add(layers.Activation('relu')) model.add(layers.Flatten()) model.add(layers.Dense(1164)) model.add(layers.Activation('relu')) model.add(layers.Dropout(0.2)) model.add(layers.Dense(100)) model.add(layers.Activation('relu')) model.add(layers.Dropout(0.2)) model.add(layers.Dense(50)) model.add(layers.Activation('relu')) model.add(layers.Dropout(0.2)) model.add(layers.Dense(10)) model.add(layers.Activation('relu')) model.add(layers.Dropout(0.2)) model.add(layers.Dense(1)) model.add(layers.Activation('linear')) model.compile(optimizer = 'adam', loss= 'mse', metrics=['accuracy']) epochs = 30 batchSize = 100 xs, ys = LoadTrainSet() print("train batch loaded") x, y = LoadTestSet() print("test batch loaded") xs = np.array(xs) x = np.array(x) #ys = tf.math.l2_normalize(np.array(ys)) #y = tf.math.l2_normalize(np.array(y)) #(Suggestion by Simon)Replaced by: epsilon = 1e-12 ys = np.array(ys) / tf.math.sqrt(tf.math.reduce_mean(np.array(ys)**2), epsilon) y = np.array(y) / tf.math.sqrt(tf.math.reduce_mean(np.array(y)**2), epsilon) history = model.fit(xs, ys, batch_size=batchSize, epochs=epochs) testLoss, testAcc = model.evaluate(x, y, verbose=2)

培训：

Epoch 4/30
5001/5001 [==============================] - 9s 2ms/sample - loss: 1.9974e-04 - accuracy: 0.0382
Epoch 5/30
5001/5001 [==============================] - 9s 2ms/sample - loss: 2.0004e-04 - accuracy: 0.0382
Epoch 6/30
5001/5001 [==============================] - 8s 2ms/sample - loss: 2.0040e-04 - accuracy: 0.0382
Epoch 7/30
5001/5001 [==============================] - 8s 2ms/sample - loss: 1.9986e-04 - accuracy: 0.0382
Epoch 8/30
5001/5001 [==============================] - 8s 2ms/sample - loss: 2.0064e-04 - accuracy: 0.0382
Epoch 9/30
5001/5001 [==============================] - 8s 2ms/sample - loss: 2.0014e-04 - accuracy: 0.0382
Epoch 10/30
5001/5001 [==============================] - 8s 2ms/sample - loss: 1.9993e-04 - accuracy: 0.0382

预测：

MODEL PREDICTIONS:
[[0.00018978]
 [0.00018978]
 [0.00018978]
 [0.00018978]
 [0.00018978]
 [0.00018978]
 [0.00018978]
 [0.00018978]
 [0.00018978]
 [0.00018978]]
ACTUAL VALUES:
[[ 0.01337768]
 [-0.00774151]
 [-0.00143646]
 [ 0.        ]
 [ 0.00287291]
 [-0.00287291]
 [ 0.        ]
 [-0.00199569]
 [ 0.02122884]
 [ 0.01083373]]

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第一次发帖，请原谅我的错误。任何帮助都将不胜感激。

因为您的标签很小（因为您的标签很小）（评论不适用于扩展讨论；此对话已结束。评论不适用于扩展讨论；此对话已结束。