TF2-callbacks

#CALLBACK(回調函數)
import tensorflow as tf
from tensorflow import keras
import matplotlib as mpl
from matplotlib import pyplot as plt
%matplotlib inline
import sklearn
import numpy as np
import pandas as pd
import os
import sys
import time
print(np.__version__)

1.16.4

#Get data:
fashion_mnist = keras.datasets.fashion_mnist                             #download the mnist data
(x_train_all,y_train_all),(x_test,y_test) = fashion_mnist.load_data()    #split data to train and test
x_valid ,x_train = x_train_all[:5000],x_train_all[5000:]                 #splot train data to train and valid
y_valid ,y_train = y_train_all[:5000],y_train_all[5000:]                 #splot train data to train and valid
print(x_valid.shape,y_valid.shape)
print(x_train.shape,y_train.shape)
print(x_test.shape,y_test.shape)

(5000, 28, 28) (5000,)
(55000, 28, 28) (55000,)
(10000, 28, 28) (10000,)

#Normalization或StandarScaler(歸一化)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()

#歸一化訓練集,歸一化輸入的是float類型的二維數據,所以先轉化爲二維數據,得到結果之後在把shape換回來
x_train_scaled = scaler.fit_transform(         
    x_train.astype(np.float32).reshape(-1,1)
).reshape(-1,28,28)
#歸一化驗證集
x_valid_scaled = scaler.transform(
    x_valid.astype(np.float32).reshape(-1,1)
).reshape(-1,28,28)
#歸一化測試集
x_test_scaled = scaler.transform(
    x_test.astype(np.float32).reshape(-1,1)
).reshape(-1,28,28)

# Tf.keras.Sequential (bulit model)
model = keras.models.Sequential()
model.add(keras.layers.Flatten(input_shape = (28,28)))
model.add(keras.layers.Dense(300,activation = "relu"))
model.add(keras.layers.Dense(100,activation = "relu"))
model.add(keras.layers.Dense(10,activation = "softmax"))

#relu : y = max(x,0)
#softmax : change vertor to probability distributions

#why sparse: y-> index  ,y ->one hot->[](vector)
model.compile(
    loss = "sparse_categorical_crossentropy",
    optimizer = "sgd",
    metrics = ["accuracy"]
            )

#TensorBoard , EarlyStopping , ModelCheckpoint
import os
logdir = './callbacks'
if not os.path.exists(logdir):
    os.mkdir(logdir)
output_model_file = os.path.join(logdir,"fashion_mnist_model.h5")
callbacks = [
    keras.callbacks.TensorBoard(logdir),
    keras.callbacks.ModelCheckpoint(
        output_model_file,
        save_best_only=True
   ),
    keras.callbacks.EarlyStopping(
        patience=5,
        min_delta = 1e-3
    ),
]

#Train
history = model.fit(
    x_train_scaled,
    y_train,
    epochs=10,
    validation_data=(x_valid_scaled,y_valid),
    callbacks=callbacks
)

Train on 55000 samples, validate on 5000 samples
Epoch 1/10
55000/55000 [==============================] - 4s 70us/sample - loss: 0.9230 - accuracy: 0.6990 - val_loss: 0.6209 - val_accuracy: 0.7898
Epoch 2/10
55000/55000 [==============================] - 4s 65us/sample - loss: 0.5812 - accuracy: 0.7980 - val_loss: 0.5220 - val_accuracy: 0.8184
Epoch 3/10
55000/55000 [==============================] - 4s 65us/sample - loss: 0.5148 - accuracy: 0.8186 - val_loss: 0.4777 - val_accuracy: 0.8352
Epoch 4/10
55000/55000 [==============================] - 3s 63us/sample - loss: 0.4780 - accuracy: 0.8311 - val_loss: 0.4546 - val_accuracy: 0.8420
Epoch 5/10
55000/55000 [==============================] - 4s 64us/sample - loss: 0.4537 - accuracy: 0.8399 - val_loss: 0.4348 - val_accuracy: 0.8502
Epoch 6/10
55000/55000 [==============================] - 4s 64us/sample - loss: 0.4357 - accuracy: 0.8460 - val_loss: 0.4250 - val_accuracy: 0.8538
Epoch 7/10
55000/55000 [==============================] - 4s 64us/sample - loss: 0.4213 - accuracy: 0.8507 - val_loss: 0.4118 - val_accuracy: 0.8578
Epoch 8/10
55000/55000 [==============================] - 4s 64us/sample - loss: 0.4095 - accuracy: 0.8550 - val_loss: 0.4027 - val_accuracy: 0.8618
Epoch 9/10
55000/55000 [==============================] - 4s 67us/sample - loss: 0.3993 - accuracy: 0.8583 - val_loss: 0.4005 - val_accuracy: 0.8582
Epoch 10/10
55000/55000 [==============================] - 4s 64us/sample - loss: 0.3907 - accuracy: 0.8614 - val_loss: 0.3871 - val_accuracy: 0.8638

#在命令行激活python-tf2環境的情況下輸入"tensorboard --logdir=callbacks" (callbacks是文件夾名稱)
#返回  TensorBoard 1.14.0a20190301 at http://lowry-MS-7A37:6006 (Press CTRL+C to quit)
# 打開了一個本地服務器