Keras + Tensorflow奇妙な結果

Question

：

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() 

を私は早く停止、チェックポイントとTensorboardコールバックを追加し、以下の結果持っている：

を

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 

ログによると、私のモデルの精度は0.81496です。奇妙なことは、検証精度がトレーニング精度（0.81対0.76）より高く、検証損失がトレーニング損失（0.41対0.47）よりも低いことです。

Q：は、私が何をしないのです、私はこの問題を解決するために、自分のコードに変更すること何が必要ですか？

Answer 1

データをシャッフルすると、問題は解決されます。

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()