将上面代码结果用进行可视化并用Python写出来

以下是将上面代码结果进行可视化的代码：

import matplotlib.pyplot as plt

plt.plot(history.history['loss'])
plt.plot(history.history['accuracy'])
plt.title('Training Loss and Accuracy')
plt.xlabel('Epoch')
plt.legend(['Loss', 'Accuracy'])
plt.show()

以下是用Python写出来的完整代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, LSTM, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

# Load the data
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Normalize the pixel values
x_train, x_test = x_train / 255.0, x_test / 255.0

# Define the model architecture
inputs = Input(shape=(28, 28))
x = LSTM(128)(inputs)
x = Dropout(0.5)(x)
outputs = Dense(10, activation='softmax')(x)

model = Model(inputs=inputs, outputs=outputs)

# Compile the model
model.compile(loss='sparse_categorical_crossentropy',
              optimizer=Adam(learning_rate=0.001),
              metrics=['accuracy'])

# Train the model
history = model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

# Visualize the training loss and accuracy
import matplotlib.pyplot as plt

plt.plot(history.history['loss'])
plt.plot(history.history['accuracy'])
plt.title('Training Loss and Accuracy')
plt.xlabel('Epoch')
plt.legend(['Loss', 'Accuracy'])
plt.show()

运行上面代码后，可以看到训练过程中每个epoch的loss和accuracy的变化。