summary_metric = {} raw_metrics = utils.init_metrics(sample=sample)

这看起来像是 Python 代码中的两行声明语句。第一行创建了一个空字典 `summary_metric`，而第二行调用了一个 `utils` 模块中的函数 `init_metrics()`，将返回值赋给了变量 `raw_metrics`。根据函数名和参数名，可以猜测这个函数的作用是初始化一些度量指标，例如用于评估模型性能的精度、召回率等指标。`sample` 参数可能是一个数据样本，用于计算这些指标。

相关问题

import numpy as np import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt Let us define a plt function for simplicity def plt_loss(x,training_metric,testing_metric,ax,colors = ['b']): ax.plot(x,training_metric,'b',label = 'Train') ax.plot(x,testing_metric,'k',label = 'Test') ax.set_xlabel('Epochs') ax.set_ylabel('Accuracy') plt.legend() plt.grid() plt.show() tf.keras.utils.set_random_seed(1) We import the Minist Dataset using Keras.datasets (train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data() We first vectorize the image (28*28) into a vector （784） train_data = train_data.reshape(train_data.shape[0],train_data.shape[1]train_data.shape[2]) # 60000784 test_data = test_data.reshape(test_data.shape[0],test_data.shape[1]test_data.shape[2]) # 10000784 We next change label number to a 10 dimensional vector, e.g., 1-> train_labels = keras.utils.to_categorical(train_labels,10) test_labels = keras.utils.to_categorical(test_labels,10) start to build a MLP model N_batch_size = 5000 N_epochs = 100 lr = 0.01 we build a three layer model, 784 -> 64 -> 10 MLP_3 = keras.models.Sequential([ keras.layers.Dense(128, input_shape=(784,),activation='relu'), keras.layers.Dense(64, activation='relu'), keras.layers.Dense(10,activation='softmax') ]) MLP_3.compile( optimizer=keras.optimizers.Adam(lr), loss= 'categorical_crossentropy', metrics = ['accuracy'] ) History = MLP_3.fit(train_data,train_labels, batch_size = N_batch_size, epochs = N_epochs,validation_data=(test_data,test_labels), shuffle=False) train_acc = History.history['accuracy'] test_acc = History.history对于该模型，使用不同数量的训练数据（5000，10000，15000，…，60000,公差=5000的等差数列），绘制训练集和测试集准确率（纵轴）关于训练数据大小(横轴)的曲线

Sure, here's the code:

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

# Define a plt function for simplicity
def plt_loss(x,training_metric,testing_metric,ax,colors = ['b']):
    ax.plot(x,training_metric,'b',label = 'Train')
    ax.plot(x,testing_metric,'k',label = 'Test')
    ax.set_xlabel('Epochs')
    ax.set_ylabel('Accuracy')
    plt.legend()
    plt.grid()
    plt.show()

tf.keras.utils.set_random_seed(1)

# Import the MNIST Dataset using Keras.datasets
(train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data()

# Vectorize the image (28*28) into a vector （784）
train_data = train_data.reshape(train_data.shape[0],train_data.shape[1]*train_data.shape[2]) # 60000*784
test_data = test_data.reshape(test_data.shape[0],test_data.shape[1]*test_data.shape[2]) # 10000*784

# Change label number to a 10 dimensional vector, e.g., 1-> [0,1,0,0,0,0,0,0,0,0]
train_labels = keras.utils.to_categorical(train_labels,10)
test_labels = keras.utils.to_categorical(test_labels,10)

# Start to build a MLP model
N_batch_size = 5000
N_epochs = 100
lr = 0.01

# Build a three layer model, 784 -> 64 -> 10
MLP_3 = keras.models.Sequential([
    keras.layers.Dense(128, input_shape=(784,),activation='relu'),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(10,activation='softmax')
])

MLP_3.compile(
    optimizer=keras.optimizers.Adam(lr),
    loss= 'categorical_crossentropy',
    metrics = ['accuracy']
)

# Store the training history
train_acc = []
test_acc = []

for i in range(5000,65000,5000):
    print('Training on',i,'samples')
    History = MLP_3.fit(train_data[:i],train_labels[:i], batch_size = N_batch_size, epochs = N_epochs,validation_data=(test_data,test_labels), shuffle=False)
    train_acc.append(History.history['accuracy'][-1])
    test_acc.append(History.history['val_accuracy'][-1])

# Plot the training and testing accuracy as a function of training data size
plt.figure(figsize=(8,5))
plt.plot(range(5000,65000,5000),train_acc,'b',label = 'Train')
plt.plot(range(5000,65000,5000),test_acc,'k',label = 'Test')
plt.xlabel('Training Data Size')
plt.ylabel('Accuracy')
plt.legend()
plt.grid()
plt.show()

This code trains a three-layer MLP model on the MNIST dataset using Keras. It then loops through different numbers of training samples (5000, 10000, 15000, ..., 60000) and trains the model on each subset of the data for 100 epochs. After each training run, it records the final training and testing accuracy. Finally, it plots a graph of accuracy as a function of training data size.

import numpy as np import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt ## Let us define a plt function for simplicity def plt_loss(x,training_metric,testing_metric,ax,colors = ['b']): ax.plot(x,training_metric,'b',label = 'Train') ax.plot(x,testing_metric,'k',label = 'Test') ax.set_xlabel('Epochs') ax.set_ylabel('Accuarcy')# ax.set_ylabel('Categorical Crossentropy Loss') plt.legend() plt.grid() plt.show() tf.keras.utils.set_random_seed(1) ## We import the Minist Dataset using Keras.datasets (train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data() ## We first vectorize the image (28*28) into a vector （784） train_data = train_data.reshape(train_data.shape[0],train_data.shape[1]train_data.shape[2]) # 60000784 test_data = test_data.reshape(test_data.shape[0],test_data.shape[1]test_data.shape[2]) # 10000784 ## We next change label number to a 10 dimensional vector, e.g., 1->[0,1,0,0,0,0,0,0,0,0] train_labels = keras.utils.to_categorical(train_labels,10) test_labels = keras.utils.to_categorical(test_labels,10) ## start to build a MLP model N_batch_size = 5000 N_epochs = 100 lr = 0.01 ## we build a three layer model, 784 -> 64 -> 10 MLP_4 = keras.models.Sequential([ keras.layers.Dense(128, input_shape=(784,),activation='relu'), keras.layers.Dense(64,activation='relu'), keras.layers.Dense(10,activation='softmax') ]) MLP_4.compile( optimizer=keras.optimizers.Adam(lr), loss= 'categorical_crossentropy', metrics = ['accuracy'] ) History = MLP_4.fit(train_data[:10000],train_labels[:10000], batch_size = N_batch_size, epochs = N_epochs,validation_data=(test_data,test_labels), shuffle=False) train_acc = History.history['accuracy'] test_acc = History.history['val_accuracy']在该模型中加入early stopping，使用monitor='loss', patience = 2设置代码

Sure, here is the updated code with early stopping:

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

## Let us define a plt function for simplicity 
def plt_loss(x,training_metric,testing_metric,ax,colors = ['b']): 
    ax.plot(x,training_metric,'b',label = 'Train') 
    ax.plot(x,testing_metric,'k',label = 'Test') 
    ax.set_xlabel('Epochs') 
    ax.set_ylabel('Accuarcy')# ax.set_ylabel('Categorical Crossentropy Loss') 
    plt.legend() 
    plt.grid() 
    plt.show() 

tf.keras.utils.set_random_seed(1) 

## We import the Minist Dataset using Keras.datasets 
(train_data, train_labels), (test_data, test_labels) = keras.datasets.mnist.load_data() 

## We first vectorize the image (28*28) into a vector （784） 
train_data = train_data.reshape(train_data.shape[0],train_data.shape[1]*train_data.shape[2]) # 60000*784 
test_data = test_data.reshape(test_data.shape[0],test_data.shape[1]*test_data.shape[2]) # 10000*784 

## We next change label number to a 10 dimensional vector, e.g., 1->[0,1,0,0,0,0,0,0,0,0] 
train_labels = keras.utils.to_categorical(train_labels,10) 
test_labels = keras.utils.to_categorical(test_labels,10) 

## start to build a MLP model 
N_batch_size = 5000 
N_epochs = 100 
lr = 0.01 

## we build a three layer model, 784 -> 64 -> 10 
MLP_4 = keras.models.Sequential([ 
    keras.layers.Dense(128, input_shape=(784,),activation='relu'), 
    keras.layers.Dense(64,activation='relu'), 
    keras.layers.Dense(10,activation='softmax') 
]) 

MLP_4.compile( 
    optimizer=keras.optimizers.Adam(lr), 
    loss= 'categorical_crossentropy', 
    metrics = ['accuracy'] 
) 

## Add early stopping 
early_stop = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=2)

History = MLP_4.fit(train_data[:10000],train_labels[:10000], 
                    batch_size = N_batch_size, 
                    epochs = N_epochs,
                    validation_data=(test_data,test_labels), 
                    shuffle=False,
                    callbacks=[early_stop])

train_acc = History.history['accuracy'] 
test_acc = History.history['val_accuracy']

In this code, we added the early stopping callback by creating an instance of `tf.keras.callbacks.EarlyStopping` and passing it to the `callbacks` argument of the `fit` method. We set the `monitor` parameter to `'loss'` and the `patience` parameter to `2`. This means that training will stop if the loss does not improve after 2 epochs.