如何将新数字手写训练到现有的keras模型中？

Question

我有一个经过训练可以识别手写数字的现有模型。然后我有一个新的样本数字来训练更多的模型。有什么办法吗？

  import os
  import cv2 as cv
  import numpy as np
  import matplotlib.pyplot as plt
  import tensorflow as tf

  ### train model ###
  mnist = tf.keras.datasets.mnist
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
  x_train = x_train/255
  x_test = x_test/255

  model = tf.keras.models.Sequential()
  model.add(tf.keras.layers.Flatten(input_shape=(28,28)))
  model.add(tf.keras.layers.Dense(units=128,activation=tf.nn.relu))
  model.add(tf.keras.layers.Dense(units=10,activation=tf.nn.softmax))

  model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])

  model.fit(x_train, y_train, epochs=5)
  model.save('traineddata.model')

我的新样本是 1 号，如下所示：

Answer 1

您可以加载图像、转换为灰度、调整大小为 (28,28)
并用一个示例转换为训练数组，并将其与 fit()

一起使用

x_example = cv2.imread('image.png')
x_example = cv2.cvtColor(x_example, cv2.COLOR_BGR2GRAY)
x_example = cv2.resize(x_example, (28, 28))

y_example = 1

x_data = np.array( [ x_example ] )  # it has to be array with shape (1, 28, 28) instead of (28, 28)
y_data = np.array( [ y_example ] )  # it has to be array with shape (1, 1) instead of (1,)

model.fit(x_data, y_data, epochs=5)

但它不能很好地预测 epochs=5。
对于 epochs=10 它给出了这张图片的正确预测，但我没有检查它是否仍然给出了其他图片的正确预测。

也许将图像添加到 x_train、y_train 并重新训练会更好。

x_data = np.append(x_train, [x_example], axis=0)
y_data = np.append(y_train, y_example)

model.fit(x_data, y_data, epochs=5)

它可以像在现实生活中一样 - 当您学习新元素时，您最好记住它，而不是旧元素。当您用新元素再次学习所有元素时，您会刷新所有信息并记住所有这些信息。

我用于测试的最小工作代码。

import warnings

warnings.filterwarnings('ignore')  # hide/supress warnings

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

### train model ###

def build():
    print('-'*50)
    print('# Building model ')
    print('-'*50)
    
    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.Flatten(input_shape=(28, 28)))
    model.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
    model.add(tf.keras.layers.Dense(units=10, activation=tf.nn.softmax))
    
    model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])

    return model
    
def train(model, x_train, y_train, epochs=5):
    print('-'*50)
    print('# Training model')
    print('-'*50)
    
    model.fit(x_train, y_train, epochs=epochs)

def save(model):
    print('-'*50)
    print('# Saving model')
    print('-'*50)

    model.save('traineddata.model')

def load():
    print('-'*50)
    print('# Loading model')
    print('-'*50)
    
    return tf.keras.models.load_model('traineddata.model')

def test_one(model, x_example, y_example):
    print('-'*50)
    print('# Testing one element')
    print('-'*50)
    
    # create array with one or more images
    x_data = np.array( [ x_example ] )
    y_data = np.array( [ y_example ] )

    print('x_data shape:', x_data.shape)
    print('y_data shape:', y_data.shape)
    print(' expected:', y_data)

    # get list with one or more predictions
    y_results = model.predict(x_data)

    print('predicted:', y_results.argmax(axis=1))

def retrain_one(model, x_example, y_example, epochs=5):
    print('-'*50)
    print('# Retraining one element')
    print('-'*50)

    # create array with one or more images
    x_data = np.array( [ x_example ] )
    y_data = np.array( [ y_example ] )

    print('x_data shape:', x_data.shape)
    print('y_data shape:', y_data.shape)
    print('y_data:', y_data)

    model.fit(x_data, y_data, epochs=epochs)

def retrain_all(model, x_train, y_train, x_example, y_example, epochs=5):
    print('-'*50)
    print('# Retraining all elements')
    print('-'*50)

    # create array with all images
    x_data = np.append(x_train, [x_example], axis=0)
    y_data = np.append(y_train, y_example)

    print('x_data shape:', x_data.shape)
    print('y_data shape:', y_data.shape)
    print('y_data:', y_data)

    model.fit(x_data, y_data, epochs=epochs)

# --- main ---

# - load train/test images -

print('>>> Loading train/test data ...')
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

x_train = x_train/255
x_test = x_test/255

# - train + save or load -

if not os.path.exists('traineddata.model'):
    print('>>> Building model ...')
    model = build()

    print('>>> Training model ...')
    train(model, x_train, y_train)

    print('>>> Saving model ...')
    save(model)
else:
    print('>>> Loading model ...')
    model = load()

#print(' - test on single example - ')
#index = 0
#test_one(model, x_train[index], y_train[index])

print(' - image - ')

x_example = cv2.imread('image.png')
x_example = cv2.cvtColor(x_example, cv2.COLOR_BGR2GRAY)
x_example = cv2.resize(x_example, (28, 28))

y_example = 1

print('>>> Predicting without training')

test_one(model, x_example, y_example)

print('>>> Predicting with training one element (epochs=10)')

retrain_one(model, x_example, y_example, epochs=10)  # epochs=5 epochs=7
test_one(model, x_example, y_example)

print('>>> Predicting with retraining all elements')

retrain_all(model, x_train, y_train, x_example, y_example)
test_one(model, x_example, y_example)

#print('>>> Saving new model')
#model.save('traineddata.model')

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