解释一下:model = ResNet18([2, 2, 2, 2]) model.build(input_shape=(128, 32, 32, 1)) model.compile(optimizer = tf.keras.optimizers.Adam(lr = 1e-3), loss = tf.keras.losses.MSE, metrics = ['MSE']) checkpoint_save_path = "./checkpoint/InceptionNet_im_3/checkpoint.ckpt"
时间: 2023-06-19 08:07:03 浏览: 44
这段代码定义了一个名为`model`的ResNet18模型,并使用给定的超参数和输入形状来构建模型。其中`[2, 2, 2, 2]`表示ResNet18中每个残差块里有2个卷积层,一共有4个残差块,因此有16个卷积层。`input_shape=(128, 32, 32, 1)`表示输入数据的形状是`(batch_size, height, width, channels)`,其中`batch_size=128`,`height=32`,`width=32`,`channels=1`。模型使用Adam优化器和均方误差损失函数进行编译,同时计算均方误差指标。最后,将模型的checkpoint保存路径设置为`"./checkpoint/InceptionNet_im_3/checkpoint.ckpt"`,用于在训练过程中保存模型的权重。
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解释一下这段代码:import tensorflow as tf from im_dataset import train_image, train_label, test_image, test_label from Resnet18 import ResNet18 import os import matplotlib.pyplot as plt import argparse import numpy as np train_db = tf.data.Dataset.from_tensor_slices((train_image, train_label)) test_db = tf.data.Dataset.from_tensor_slices((test_image, test_label)) model = ResNet18([2, 2, 2, 2]) model.build(input_shape=(128, 32, 32, 1)) model.compile(optimizer = tf.keras.optimizers.Adam(lr = 1e-3), loss = tf.keras.losses.MSE, metrics = ['MSE']) checkpoint_save_path = "./checkpoint/InceptionNet_im_3/checkpoint.ckpt" if os.path.exists(checkpoint_save_path+'.index'): print('------------------load the model---------------------') model.load_weights(checkpoint_save_path) cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,save_weights_only=True,save_best_only=True) history = model.fit(train_db, epochs=500,batch_size=128, validation_data=test_db, validation_freq=1, callbacks=[cp_callback]) model.summary() acc = history.history['loss'] val_acc = history.history['val_loss'] plt.plot(acc, label='Training MSE') plt.plot(val_acc, label='Validation MSE') plt.title('Training and Validation MSE') plt.legend() plt.show()
这段代码实现了使用 ResNet18 模型对图像进行分类和训练。首先通过导入 TensorFlow 和相关模块,加载训练和测试数据。之后,使用 ResNet18 模型构建一个神经网络,并编译模型,设置优化器和损失函数。接着,判断是否存在已经保存的模型,如果存在则加载模型。接下来,定义一个回调函数来保存训练过程中的最佳模型。最后,使用 fit() 函数对模型进行训练,并可视化训练和验证数据的 MSE 损失函数。模型训练完成后,使用 model.summary() 函数来打印出模型的架构信息。
解释import tensorflow as tf from im_dataset import train_image, train_label, test_image, test_label from AlexNet8 import AlexNet8 from baseline import baseline from InceptionNet import Inception10 from Resnet18 import ResNet18 import os import matplotlib.pyplot as plt import argparse import numpy as np parse = argparse.ArgumentParser(description="CVAE model for generation of metamaterial") hyperparameter_set = parse.add_argument_group(title='HyperParameter Setting') dim_set = parse.add_argument_group(title='Dim setting') hyperparameter_set.add_argument("--num_epochs",type=int,default=200,help="Number of train epochs") hyperparameter_set.add_argument("--learning_rate",type=float,default=4e-3,help="learning rate") hyperparameter_set.add_argument("--image_size",type=int,default=16*16,help="vector size of image") hyperparameter_set.add_argument("--batch_size",type=int,default=16,help="batch size of database") dim_set.add_argument("--z_dim",type=int,default=20,help="dim of latent variable") dim_set.add_argument("--feature_dim",type=int,default=32,help="dim of feature vector") dim_set.add_argument("--phase_curve_dim",type=int,default=41,help="dim of phase curve vector") dim_set.add_argument("--image_dim",type=int,default=16,help="image size: [image_dim,image_dim,1]") args = parse.parse_args() def preprocess(x, y): x = tf.io.read_file(x) x = tf.image.decode_png(x, channels=1) x = tf.cast(x,dtype=tf.float32) /255. x1 = tf.concat([x, x], 0) x2 = tf.concat([x1, x1], 1) x = x - 0.5 y = tf.convert_to_tensor(y) y = tf.cast(y,dtype=tf.float32) return x2, y train_db = tf.data.Dataset.from_tensor_slices((train_image, train_label)) train_db = train_db.shuffle(100).map(preprocess).batch(args.batch_size) test_db = tf.data.Dataset.from_tensor_slices((test_image, test_label)) test_db = test_db.map(preprocess).batch(args.batch_size) model = ResNet18([2, 2, 2, 2]) model.build(input_shape=(args.batch_size, 32, 32, 1)) model.compile(optimizer = tf.keras.optimizers.Adam(lr = 1e-3), loss = tf.keras.losses.MSE, metrics = ['MSE']) checkpoint_save_path = "./checkpoint/InceptionNet_im_3/checkpoint.ckpt" if os.path.exists(checkpoint_save_path+'.index'): print('------------------load the model---------------------') model.load_weights(checkpoint_save_path) cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,save_weights_only=True,save_best_only=True) history = model.fit(train_db, epochs=500, validation_data=test_db, validation_freq=1, callbacks=[cp_callback]) model.summary() acc = history.history['loss'] val_acc = history.history['val_loss'] plt.plot(acc, label='Training MSE') plt.plot(val_acc, label='Validation MSE') plt.title('Training and Validation MSE') plt.legend() plt.show()
这段代码是一个使用 ResNet18 模型进行图像分类的程序,它使用了 TensorFlow 深度学习框架,其中包含了许多参数和超参数的设置。具体来说,它的输入数据是 train_image 和 train_label,输出结果是对 test_image 进行分类的预测结果。在训练过程中,它使用了 Adam 优化器和均方误差损失函数,并且使用了 ModelCheckpoint 回调函数来保存最佳的模型权重。最后,它还画出了训练和验证 MSE 的图表。