Keras + Tensorflow奇怪的结果

Question

使用Pimia印度人糖尿病数据集我已经建立了下列顺序模型：

import matplotlib.pyplot as plt 
import numpy 
from keras import callbacks 
from keras import optimizers 
from keras.layers import Dense 
from keras.models import Sequential 
from keras.callbacks import ModelCheckpoint 
from sklearn.preprocessing import StandardScaler 

#TensorBoard callback for visualization of training history 
tb = callbacks.TensorBoard(log_dir='./logs/latest', histogram_freq=10, batch_size=32, 
          write_graph=True, write_grads=True, write_images=False, 
          embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None) 


# Early stopping - Stop training before overfitting 
early_stop = callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, mode='auto') 

# fix random seed for reproducibility 
seed = 42 
numpy.random.seed(seed) 
# load pima indians dataset 
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",") 
# split into input (X) and output (Y) variables 
X = dataset[:, 0:8] 
Y = dataset[:, 8] 

# Standardize features by removing the mean and scaling to unit variance 
scaler = StandardScaler() 
X = scaler.fit_transform(X) 


#ADAM Optimizer with learning rate decay 
opt = optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0001) 

## Create our model 
model = Sequential() 

model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu')) 
model.add(Dense(8, kernel_initializer='uniform', activation='relu')) 
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid')) 

# Compile the model using binary crossentropy since we are predicting 0/1 
model.compile(loss='binary_crossentropy', 
       optimizer=opt, 
       metrics=['accuracy']) 

# checkpoint 
filepath="./checkpoints/weights.best.hdf5" 
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max') 

# Fit the model 
history = model.fit(X, Y, validation_split=0.33, epochs=10000, batch_size=10, verbose=0, callbacks=[tb,early_stop,checkpoint]) 
# list all data in history 
print(history.history.keys()) 
# summarize history for accuracy 
plt.plot(history.history['acc']) 
plt.plot(history.history['val_acc']) 
plt.title('model accuracy') 
plt.ylabel('accuracy') 
plt.xlabel('epoch') 
plt.legend(['train', 'test'], loc='upper left') 
plt.show() 
# summarize history for loss 
plt.plot(history.history['loss']) 
plt.plot(history.history['val_loss']) 
plt.title('model loss') 
plt.ylabel('loss') 
plt.xlabel('epoch') 
plt.legend(['train', 'test'], loc='upper left') 
plt.show() 

我已经添加提早终止，检查点和Tensorboard回调，并得到了以下结果：

Epoch 00000: val_acc improved from -inf to 0.67323, saving model to ./checkpoints/weights.best.hdf5 
Epoch 00001: val_acc did not improve 
... 
Epoch 00024: val_acc improved from 0.67323 to 0.67323, saving model to ./checkpoints/weights.best.hdf5 
... 
Epoch 00036: val_acc improved from 0.76378 to 0.76378, saving model to ./checkpoints/weights.best.hdf5 
... 
Epoch 00044: val_acc improved from 0.79921 to 0.80709, saving model to ./checkpoints/weights.best.hdf5 
... 
Epoch 00050: val_acc improved from 0.80709 to 0.80709, saving model to ./checkpoints/weights.best.hdf5 
... 
Epoch 00053: val_acc improved from 0.80709 to 0.81102, saving model to ./checkpoints/weights.best.hdf5 
... 
Epoch 00257: val_acc improved from 0.81102 to 0.81102, saving model to ./checkpoints/weights.best.hdf5 
... 
Epoch 00297: val_acc improved from 0.81102 to 0.81496, saving model to ./checkpoints/weights.best.hdf5 
Epoch 00298: val_acc did not improve 
Epoch 00299: val_acc did not improve 
Epoch 00300: val_acc did not improve 
Epoch 00301: val_acc did not improve 
Epoch 00302: val_acc did not improve 
Epoch 00302: early stopping 

所以根据日志，我的模型精度是0.81496。奇怪的是，验证的准确性高于训练的准确性（0.81 vs 0.76），验证损失低于训练损失（0.41 vs 0.47）。

问：我缺少什么，需要什么我的代码更改，以解决这个问题？

Answer 1

如果洗牌数据，问题就解决了。

import matplotlib.pyplot as plt 
import numpy 
from keras import callbacks 
from keras import optimizers 
from keras.layers import Dense 
from keras.models import Sequential 
from keras.callbacks import ModelCheckpoint 
from sklearn.preprocessing import StandardScaler 
from sklearn.utils import shuffle 

# TensorBoard callback for visualization of training history 
tb = callbacks.TensorBoard(log_dir='./logs/4', histogram_freq=10, batch_size=32, 
          write_graph=True, write_grads=True, write_images=False, 
          embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None) 


# Early stopping - Stop training before overfitting 
early_stop = callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, mode='auto') 

# fix random seed for reproducibility 
seed = 42 
numpy.random.seed(seed) 
# load pima indians dataset 
dataset = numpy.loadtxt("../Downloads/pima-indians-diabetes.csv", delimiter=",") 
# split into input (X) and output (Y) variables 
X = dataset[:, 0:8] 
Y = dataset[:, 8] 

# Standardize features by removing the mean and scaling to unit variance 
scaler = StandardScaler() 
X = scaler.fit_transform(X) 

# This is the important part 
X, Y = shuffle(X, Y) 

#ADAM Optimizer with learning rate decay 
opt = optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0001) 

## Create our model 
model = Sequential() 

model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu')) 
model.add(Dense(8, kernel_initializer='uniform', activation='relu')) 
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid')) 

# Compile the model using binary crossentropy since we are predicting 0/1 
model.compile(loss='binary_crossentropy', 
       optimizer=opt, 
       metrics=['accuracy']) 

# checkpoint 
# filepath="./checkpoints/weights.best.hdf5" 
# checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max') 

# Fit the model 
history = model.fit(X, Y, validation_split=0.33, epochs=1000, batch_size=10, verbose=0, callbacks=[tb,early_stop]) 
# list all data in history 
print(history.history.keys()) 
# summarize history for accuracy 
plt.plot(history.history['acc']) 
plt.plot(history.history['val_acc']) 
plt.title('model accuracy') 
plt.ylabel('accuracy') 
plt.xlabel('epoch') 
plt.legend(['train', 'test'], loc='upper left') 
plt.show() 
# summarize history for loss 
plt.plot(history.history['loss']) 
plt.plot(history.history['val_loss']) 
plt.title('model loss') 
plt.ylabel('loss') 
plt.xlabel('epoch') 
plt.legend(['train', 'test'], loc='upper left') 
plt.show()