Python Keras中MNIST数字识别中测试数据的不同精度
我正在使用Keras进行手写数字识别,我有两个文件:predict.py和train.py train.py训练模型(如果尚未训练)并将其保存到一个目录中,否则它只会从保存到的目录中加载训练过的模型,并打印Python Keras中MNIST数字识别中测试数据的不同精度,python,machine-learning,keras,classification,mnist,Python,Machine Learning,Keras,Classification,Mnist,我正在使用Keras进行手写数字识别,我有两个文件:predict.py和train.py train.py训练模型(如果尚未训练)并将其保存到一个目录中,否则它只会从保存到的目录中加载训练过的模型,并打印测试损失和测试精度 def getData(): (X_train, y_train), (X_test, y_test) = mnist.load_data() y_train = to_categorical(y_train, num_classes=10) y_t
测试损失
和测试精度
def getData():
(X_train, y_train), (X_test, y_test) = mnist.load_data()
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
X_train = X_train.reshape(X_train.shape[0], 784)
X_test = X_test.reshape(X_test.shape[0], 784)
# normalizing the data to help with the training
X_train /= 255
X_test /= 255
return X_train, y_train, X_test, y_test
def trainModel(X_train, y_train, X_test, y_test):
# training parameters
batch_size = 1
epochs = 10
# create model and add layers
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(784,)))
model.add(Dense(10, activation = 'softmax'))
# compiling the sequential model
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
# training the model and saving metrics in history
history = model.fit(X_train, y_train,
batch_size=batch_size, epochs=epochs,
verbose=2,
validation_data=(X_test, y_test))
loss_and_metrics = model.evaluate(X_test, y_test, verbose=2)
print("Test Loss", loss_and_metrics[0])
print("Test Accuracy", loss_and_metrics[1])
# Save model structure and weights
model_json = model.to_json()
with open('model.json', 'w') as json_file:
json_file.write(model_json)
model.save_weights('mnist_model.h5')
return model
def loadModel():
json_file = open('model.json', 'r')
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)
model.load_weights("mnist_model.h5")
return model
X_train, y_train, X_test, y_test = getData()
if(not os.path.exists('mnist_model.h5')):
model = trainModel(X_train, y_train, X_test, y_test)
print('trained model')
print(model.summary())
else:
model = loadModel()
print('loaded model')
print(model.summary())
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
loss_and_metrics = model.evaluate(X_test, y_test, verbose=2)
print("Test Loss", loss_and_metrics[0])
print("Test Accuracy", loss_and_metrics[1])
以下是输出(假设模型已提前训练,这次只加载模型):
(“测试损失”,1.741784990310669)
(“测试精度”,0.414)
另一方面,predict.py预测手写数字:
def loadModel():
json_file = open('model.json', 'r')
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)
model.load_weights("mnist_model.h5")
return model
model = loadModel()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
(X_train, y_train), (X_test, y_test) = mnist.load_data()
y_test = to_categorical(y_test, num_classes=10)
X_test = X_test.reshape(X_test.shape[0], 28*28)
loss_and_metrics = model.evaluate(X_test, y_test, verbose=2)
print("Test Loss", loss_and_metrics[0])
print("Test Accuracy", loss_and_metrics[1])
在这种情况下,令我惊讶的是,得到了以下结果:
(“测试损失”,1.838037786674995)
(“测试精度”,0.8856)
在第二个文件中,我得到了测试精度
为0.88(是我之前得到的两倍多)
另外,model.summy()
在两个文件中都是相同的:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 64) 50240
_________________________________________________________________
dense_2 (Dense) (None, 10) 650
=================================================================
Total params: 50,890
Trainable params: 50,890
Non-trainable params: 0
_________________________________________________________________
我想不出这种行为背后的原因。这正常吗?或者我遗漏了什么?这种差异是因为一次您使用规范化数据(即除以255)调用
evaluate()
方法,而另一次(即在“predict.py”文件中)使用非规范化数据调用它。在推断时间(即测试时间)中,应始终使用与训练数据相同的预处理步骤
此外,首先将数据转换为浮点,然后将其除以255(否则,在Python 2.x和Python 3.x中使用x\u train/=255
和x\u test/=255
进行真正的除法):
在训练模型之前你没有做任何预处理吗?我做了。编辑了我的问题(我现在已经包含了完整的文件),我猜您正在使用Python 2.x?是的,
Python 2.7.15rc1
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255.
X_test /= 255.