帮我写一个基于深度学习的数字识别的pychram代码

好的，以下是一个基于深度学习的数字识别的 PyCharm 代码：

首先，需要导入必要的库：

import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

接着，准备训练数据和测试数据，这里我们使用 keras 内置的 mnist 数据集：

mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

对数据进行预处理，将像素值缩放到 0-1 之间：

train_images = train_images / 255.0
test_images = test_images / 255.0

建立模型，这里我们使用一个简单的深度学习模型，包含两个卷积层和两个全连接层：

model = keras.Sequential([
    keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),
    keras.layers.MaxPooling2D((2,2)),
    keras.layers.Conv2D(64, (3,3), activation='relu'),
    keras.layers.MaxPooling2D((2,2)),
    keras.layers.Flatten(),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(10)
])

编译模型，设置损失函数和优化器：

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

训练模型：

model.fit(train_images, train_labels, epochs=5)

评估模型：

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)

最后，使用模型进行预测：

predictions = model.predict(test_images)

完整代码如下：

import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

train_images = train_images / 255.0
test_images = test_images / 255.0

model = keras.Sequential([
    keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),
    keras.layers.MaxPooling2D((2,2)),
    keras.layers.Conv2D(64, (3,3), activation='relu'),
    keras.layers.MaxPooling2D((2,2)),
    keras.layers.Flatten(),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(10)
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=5)

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)

predictions = model.predict(test_images)