同学你好，抱歉回复的晚了。在tensorflow中就没有placeholder和sess的概念了，取而代之的是更加python化的使用方式，即直接将模型当做一个函数来使用，把数据输入给封装好的模型就可以了。
下面是一个例子，感兴趣的话可以关注我的另一门课：《tensorflow2.0》https://coding.imooc.com/class/344.html
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
print(module.__name__, module.__version__)
from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()
print(housing.DESCR)
print(housing.data.shape)
print(housing.target.shape)
from sklearn.model_selection import train_test_split
x_train_all, x_test, y_train_all, y_test = train_test_split(
housing.data, housing.target, random_state = 7)
x_train, x_valid, y_train, y_valid = train_test_split(
x_train_all, y_train_all, random_state = 11)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)
# 1. batch 遍历训练集 metric
# 1.1 自动求导
# 2. epoch结束 验证集 metric
epochs = 100
batch_size = 32
steps_per_epoch = len(x_train_scaled) // batch_size
optimizer = keras.optimizers.SGD()
metric = keras.metrics.MeanSquaredError()
def random_batch(x, y, batch_size=32):
idx = np.random.randint(0, len(x), size=batch_size)
return x[idx], y[idx]
model = keras.models.Sequential([
keras.layers.Dense(30, activation='relu',
input_shape=x_train.shape[1:]),
keras.layers.Dense(1),
])
for epoch in range(epochs):
metric.reset_states()
for step in range(steps_per_epoch):
x_batch, y_batch = random_batch(x_train_scaled, y_train,
batch_size)
with tf.GradientTape() as tape:
y_pred = model(x_batch)
y_pred = tf.squeeze(y_pred, 1)
loss = keras.losses.mean_squared_error(y_batch, y_pred)
metric(y_batch, y_pred)
grads = tape.gradient(loss, model.variables)
grads_and_vars = zip(grads, model.variables)
optimizer.apply_gradients(grads_and_vars)
print("\rEpoch", epoch, " train mse:",
metric.result().numpy(), end="")
y_valid_pred = model(x_valid_scaled)
y_valid_pred = tf.squeeze(y_valid_pred, 1)
valid_loss = keras.losses.mean_squared_error(y_valid_pred, y_valid)
print("\t", "valid mse: ", valid_loss.numpy())
