Keras模型的输出相同 [英] Same output of the Keras model

问题描述

我有一个Keras模型，可以预测游戏中的移动.我的输入形状为(160,120 ,1).我有以下模型，其输出为9个节点:

I have a Keras model for predicting moves in the game. I have an input shape of (160,120 ,1). I have the following model with an output of 9 nodes:

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers.normalization import BatchNormalization
from keras.optimizers import Adam
from keras.regularizers import l2
from keras import optimizers
def alexnet_model(n_classes=9, l2_reg=0.,
    weights=None):

    # Initialize model
    alexnet = Sequential()
    alexnet.add(Conv2D(24, (11, 11), input_shape=(160,120,1), activation ='relu'))
    alexnet.add(MaxPooling2D(pool_size=(2, 2)))
    alexnet.add(BatchNormalization())
    alexnet.add(Conv2D(36, (5, 5), activation ='relu'))
    alexnet.add(MaxPooling2D(pool_size=(2, 2)))
    alexnet.add(Conv2D(48, (3, 3),  activation ='relu'))
    alexnet.add(Conv2D(54, (3, 3),  activation ='relu'))
    alexnet.add(MaxPooling2D(pool_size=(2, 2)))
    alexnet.add(Flatten())
    alexnet.add(Dense(300,   activation ='tanh'))
    alexnet.add(Dropout(0.5))
    alexnet.add(Dense(200,   activation ='tanh'))
    alexnet.add(Dropout(0.5))
    alexnet.add(Dense(100,   activation ='tanh'))
    alexnet.add(Dropout(0.5))


    alexnet.add(Dense(n_classes , activation = 'softmax'))

    optimizer = Adam(lr=1e-3)

    alexnet.compile(loss='categorical_crossentropy', optimizer=optimizer)


    alexnet.summary()


    return alexnet

然后，我运行一个训练脚本.我的X的形状为(12862, 160, 120, 1)，y的形状为(1000,9).

Then, I run a training script. My X has a shape of (12862, 160, 120, 1) and y of (1000,9).

import numpy as np
import tensorflow as tf
from random import shuffle
import pandas as pd
from tensorflow.keras import layers,models
from keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
# what to start at
START_NUMBER = 60

# what to end at
hm_data = 111

# use a previous model to begin?
START_FRESH = False
WIDTH = 160
HEIGHT = 120
LR = 1e-3
EPOCHS = 1

MODEL_NAME = 'model_new.h5'
EXISTING_MODEL_NAME = ''

model = alexnet_model()

X=[]

Y=[]
for i in range(EPOCHS):
    train_data = np.load('training_data_1.npy')
    print(len(train_data))
    train = train_data[0:12862]
    test = train_data[-1000:]

    X = np.array([i[0] for i in train]).reshape(-1,WIDTH,HEIGHT,1)
    Y = np.array([i[1] for i in train])

    test_x = np.array([i[0] for i in test]).reshape(-1,WIDTH,HEIGHT,1)
    test_y = np.array([i[1] for i in test])
    print(X.shape)
    model.fit(X, Y , batch_size = 16, epochs = 10 , validation_data = (test_x, test_y), verbose=1)
    model.save(MODEL_NAME)

# tensorboard --logdir=foo:C:/Users/H/Desktop/ai-gaming-phase5/log

测试模型后，我得到一个输出:

After testing the model I get an output:

array([[2.8518048e-01, 5.5075828e-03, 7.3730588e-02, 5.3255934e-02,
        1.0635615e-01, 6.4690344e-02, 9.1519929e-08, 7.0413840e-08,
        4.1127869e-01]], dtype=float32)

具有以下代码行:

model.predict(X[100].reshape(-1,160,120,1)) 

我知道在X上测试模型不是很好，但是我使用哪张图片都没有关系，但是我得到相同的输出.仅供参考(我的Y值):

I know that it is not good to test model on X but it doesn't matter which picture I use but I get the same output. Just for reference (my Y values):

w = [1,0,0,0,0,0,0,0,0]
s = [0,1,0,0,0,0,0,0,0]
a = [0,0,1,0,0,0,0,0,0]
d = [0,0,0,1,0,0,0,0,0]
wa = [0,0,0,0,1,0,0,0,0]
wd = [0,0,0,0,0,1,0,0,0]
sa = [0,0,0,0,0,0,1,0,0]
sd = [0,0,0,0,0,0,0,1,0]
nk = [0,0,0,0,0,0,0,0,1]

我尝试了另一种模式，但仍然无法正常工作.这是每个课程的培训数据量:

I tried another model but it still doesn't work. Here is the amount of training data for each class:

Counter({'[1, 0, 0, 0, 0, 0, 0, 0, 0]': 5000,
         '[0, 0, 0, 0, 0, 0, 0, 0, 1]': 5000,
         '[0, 0, 0, 0, 1, 0, 0, 0, 0]': 1183,
         '[0, 0, 0, 0, 0, 1, 0, 0, 0]': 982,
         '[0, 0, 1, 0, 0, 0, 0, 0, 0]': 832,
         '[0, 0, 0, 1, 0, 0, 0, 0, 0]': 764,
         '[0, 1, 0, 0, 0, 0, 0, 0, 0]': 101})

我认为问题出在模型上，但我不知道如何更改.可能是培训数据少的问题吗?损失价值也没有下降:loss: 1.7416 - val_loss: 1.4639.它只会减少几位小数，有时甚至还会增加.

I think that the problem is in the model but I don't know how to change it. Could it be the problem of small training data? The loss valus is also not going down: loss: 1.7416 - val_loss: 1.4639. It only decreases by a few decimals and sometimes even goes back up.

推荐答案

已解决！ 仅仅标准化训练数据是行不通的.我减少了节点和层的数量，一切正常.我想这是一个过拟合的问题.

Solved! Just normalizing training data didn't work. I decreased amount of nodes and layers and everything worked fine. I guess it was an overfitting problem.

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