有许多技术，不仅模型
1.增加数据的大小，这也会产生大量的输入。
1.使用特征提取功能和数据预处理、MFCC、Furriers和数据输入转换（模糊、旋转、翻转、填充、缩放或随机噪声）。
1.创建多组数据输入并训练K折叠验证。
1.随机数据选择、保存和加载模型或性能回调参数调整。
1.在模型或级连模型之间进行比较。
示例：我的图像识别模板，在执行功能和讨论解决方案时非常有用。

import os
from os.path import exists

import tensorflow as tf
import tensorflow_io as tfio

import pandas as pd

import matplotlib.pyplot as plt

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
None
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
print(physical_devices)
print(config)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
variables = pd.read_excel('F:\\temp\\Python\\excel\\Book 13 (2) (3).xlsx', index_col=None, header=[0])

list_label = [ ]
list_Image = [ ]
list_file_actual = [ ]
list_label_actual = [ 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy' ]

for Index, Image, Label in variables.values:
    print( Label )
    # list_label.append( Label )
    
    image = tf.io.read_file( Image )
    image = tf.io.decode_image(image)
    list_file_actual.append(image)
    image = tf.image.resize(image, [32,32], method='nearest')
    list_Image.append(image)
    
    if Label == 0:
        list_label.append(0)
    else:
        list_label.append(9)
    
    # if Label == 0:
        # list_label_actual.append('Candidt Kibt')
    # else:
        # list_label_actual.append('Pikaploy')

list_label = tf.cast( list_label, dtype=tf.int32 )
list_label = tf.constant( list_label, shape=( 54, 1, 1 ) )
list_Image = tf.cast( list_Image, dtype=tf.int32 )
list_Image = tf.constant( list_Image, shape=( 54, 1, 32, 32, 3 ) )

# print( list_label_actual )
# print( list_label )

checkpoint_path = "F:\\models\\checkpoint\\" + os.path.basename(__file__).split('.')[0] + "\\TF_DataSets_01.h5"
checkpoint_dir = os.path.dirname(checkpoint_path)
loggings = "F:\\models\\checkpoint\\" + os.path.basename(__file__).split('.')[0] + "\\loggings.log"

if not exists(checkpoint_dir) : 
    os.mkdir(checkpoint_dir)
    print("Create directory: " + checkpoint_dir)
    
log_dir = checkpoint_dir

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: DataSet
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
dataset = tf.data.Dataset.from_tensor_slices(( list_Image, list_label ))
list_Image = tf.constant( list_Image, shape=( 54, 32, 32, 3) ).numpy()

print( "===========================================" )
print( "type of variables: " )
print( type(variables) )
print( variables )
print( "variables.values: " )
print( variables.values )

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
    tf.keras.layers.InputLayer(input_shape=( 32, 32, 3 )),
    tf.keras.layers.Normalization(mean=3., variance=2.),
    tf.keras.layers.Normalization(mean=4., variance=6.),
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dropout(0.3),
    tf.keras.layers.Reshape((512, 225)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96, return_sequences=True, return_state=False)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(96)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(192, activation='relu'),
    tf.keras.layers.Dense(10),
])

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: FileWriter
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
if exists(checkpoint_path) :
    model.load_weights(checkpoint_path)
    print("model load: " + checkpoint_path)
    input("Press Any Key!")

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Callback
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class custom_callback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if( logs['accuracy'] >= 0.97 ):
            self.model.stop_training = True
    
custom_callback = custom_callback()

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam(
    learning_rate=0.000001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
    name='Nadam'
)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""                               
lossfn = tf.keras.losses.SparseCategoricalCrossentropy(
    from_logits=False,
    reduction=tf.keras.losses.Reduction.AUTO,
    name='sparse_categorical_crossentropy'
)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'] )
model.save_weights(checkpoint_path)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit( dataset, batch_size=120, epochs=10000, callbacks=[custom_callback] )

plt.figure(figsize=(6, 6))
plt.title("Actors recognitions")
for i in range(30):
    img = tf.keras.preprocessing.image.array_to_img(
        list_Image[i],
        data_format=None,
        scale=True
    )
    img_array = tf.keras.preprocessing.image.img_to_array(img)
    img_array = tf.expand_dims(img_array, 0)
    predictions = model.predict(img_array)
    score = tf.nn.softmax(predictions[0])
    plt.subplot(6, 6, i + 1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(list_file_actual[i])
    plt.xlabel(str(round(score[tf.math.argmax(score).numpy()].numpy(), 2)) + ":" +  str(list_label_actual[tf.math.argmax(score)]))
    
plt.show()

input('...')

输出：他们实际上是泰国演员，从互联网上收集的广播。