简单的pycharm深度学习代码，可以直接运行，训练100个epoch，得到训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线，

下面是一个简单的PyCharm深度学习代码，可以直接运行，训练100个epoch，得到训练集和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线。我们使用Keras框架和MNIST手写数字数据集。

import numpy as np
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
from keras.utils import to_categorical
import matplotlib.pyplot as plt

batch_size = 128
num_classes = 10
epochs = 100

# Load MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Reshape data and normalize pixel values to [0, 1]
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# Convert labels to categorical one-hot encoding
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

# Define model architecture
model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))

model.summary()

# Compile model
model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])

# Train model
history = model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    validation_data=(x_test, y_test))

# Evaluate model on test data
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# Plot training and validation accuracy
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()

# Plot training and validation loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()

# Plot training and validation cross-entropy
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model cross-entropy')
plt.ylabel('Cross-entropy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')
plt.show()

上述代码中，我们首先加载MNIST数据集，并将其转换为Keras模型所需的形式。接着，我们定义了一个包含两个隐藏层的神经网络，每个隐藏层有512个神经元。我们使用Relu激活函数和Dropout正则化层来防止过拟合。在输出层，我们使用Softmax激活函数来输出每个数字类别的概率分布。

模型编译后，我们使用训练数据进行训练，并在测试数据上评估模型。然后，我们使用Matplotlib库绘制训练和验证集的准确率曲线、损失函数曲线以及交叉熵函数曲线。