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67 lines
2.3 KiB
67 lines
2.3 KiB
import tensorflow as tf
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import os
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from matplotlib import pyplot as plt
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from PIL import Image
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import numpy as np
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mnist = tf.keras.datasets.mnist
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train, x_test = x_train/255.0, x_test/255.0
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def creat_model():
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model = tf.keras.models.Sequential([
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tf.keras.layers.Flatten(),
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dense(10, activation='softmax')
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])
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model.compile(optimizer='adam',
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
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metrics=["sparse_categorical_accuracy"])
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return model
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def model_fit(model,check_save_path):
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if os.path.exists(check_save_path+'.index'):
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print("load modals...")
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model.load_weights(check_save_path)
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cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=check_save_path,
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save_weights_only=True,
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save_best_only=True)
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history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_split=0.15, validation_freq=1, callbacks=[cp_callback])
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model.summary()
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final_loss, final_acc = model.evaluate(x_test, y_test, verbose=2)
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print("Model accuracy: ", final_acc, ", model loss: ", final_loss)
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acc = history.history['sparse_categorical_accuracy']
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val_acc = history.history['val_sparse_categorical_accuracy']
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loss = history.history['loss']
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val_loss = history.history['val_loss']
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plt.subplot(1, 2, 1)
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plt.plot(acc, label='Training Accuracy')
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plt.plot(val_acc, label='Validation Accuracy')
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plt.title('Training and Validation Accuracy')
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plt.legend()
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plt.subplot(1, 2, 2)
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plt.plot(loss, label='Training Loss')
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plt.plot(val_loss, label='Validation Loss')
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plt.title('Training and Validation Loss')
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plt.legend()
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plt.show()
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def eva_acc(str, model):
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model.load_weights(str)
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final_loss, final_acc = model.evaluate(x_test, y_test, verbose=2)
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print("Model accuracy: ", final_acc, ", model loss: ", final_loss)
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if __name__=="__main__":
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check_save_path = "./checkpoint/mnist.ckpt"
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model = creat_model()
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model_fit(model, check_save_path)
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eva_acc(check_save_path, model)
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