Pimia भारतीयों मधुमेह डेटा सेट मैं निम्नलिखित अनुक्रमिक मॉडल का निर्माण किया है का उपयोग करना:Keras + Tensorflow अजीब परिणाम
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()
मैं जल्दी रोक, चौकी और Tensorboard कॉलबैक जोड़ दिया है, और निम्न परिणाम है:
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
तो लॉग के अनुसार, अपने मॉडल सटीकता ०.८१४९६ है। अजीब बात यह है कि सत्यापन सटीकता प्रशिक्षण सटीकता (0.81 बनाम 0.76) से अधिक है, और सत्यापन हानि कम है तो प्रशिक्षण हानि (0.41 बनाम 0.47)।
प्रश्न: मैं क्या याद आ रही है, क्या मैं इस समस्या को ठीक करने के लिए मेरे कोड में परिवर्तित करने के लिए की जरूरत है?
मैं डाटासेट शफ़ल अगर यह समस्या ठीक हो करने के लिए होगा। –
क्या ऐसा करने का कोई तरीका है? –
एचएम, [* यदि 'model.fit' में 'shuffle' तर्क सत्य पर सेट है (जो डिफ़ॉल्ट है), प्रशिक्षण डेटा को प्रत्येक युग * पर यादृच्छिक रूप से shuffled किया जाएगा (https://keras.io/getting -started/faq/#)-डेटा-फेरबदल के दौरान प्रशिक्षण है। –