假设我们最初通过运行以下代码创建了模型： 1import tensorflow as tf 2 3mnist = tf.keras.datasets.mnist.load_data() 4x_train...model and some data. 21 self.model = tf.keras.models.load_model(filename) 22 mnist = tf.keras.datasets.mnist.load_data...psutil.Process().cpu_affinity([i]) 16 model = tf.keras.models.load_model(filename) 17 mnist = tf.keras.datasets.mnist.load_data
(x_train, y_train), _ = tf.keras.datasets.mnist.load_data() dataset = tf.data.Dataset.from_tensor_slices...(x_train, y_train), _ = tf.keras.datasets.mnist.load_data() dataset = tf.data.Dataset.from_tensor_slices...(x_train, y_train), _ = tf.keras.datasets.mnist.load_data() dataset = tf.data.Dataset.from_tensor_slices
# 获取示例数据集，使用 MNIST 数据集，主要使用使用前1000个示例 (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data...# 获取示例数据集，使用 MNIST 数据集，主要使用使用前1000个示例 (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data
session = InteractiveSession(config=config) (train_images,train_labels),(test_images,test_labels)=tf.keras.datasets.mnist.load_data
import tensorflow as tf mnist = tf.keras.datasets.mnist.load_data()x_train, y_train = mnist[0]x_train...self.model = tf.keras.models.load_model(filename) mnist = tf.keras.datasets.mnist.load_data()...psutil.Process().cpu_affinity([i]) model = tf.keras.models.load_model(filename) mnist = tf.keras.datasets.mnist.load_data
tensorflow as tf from tensorflow.keras import layers # 加载数据集 (X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data
获取训练集和测试集 (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data( path = os.path.join
gan_output) gan.compile(optimizer='adam', loss='binary_crossentropy') # 加载MNIST数据集 (x_train, _), (_, _) = tf.keras.datasets.mnist.load_data...gan_output) gan.compile(optimizer='adam', loss='binary_crossentropy') # 加载MNIST数据集 (x_train, _), (_, _) = tf.keras.datasets.mnist.load_data...cgan.compile(optimizer='adam', loss='binary_crossentropy') # 加载MNIST数据集 (x_train, y_train), (_, _) = tf.keras.datasets.mnist.load_data
torch import torchvision b）导入并预处理数据 使用TensorFlow加载和准备数据可以使用以下两行代码： (x_trainTF_, y_trainTF_), _ = tf.keras.datasets.mnist.load_data...f）评估模型 评估模型也是如此，在TensorFlow中，您只需对测试数据调用evaluate()方法： _, (x_testTF, y_testTF)= tf.keras.datasets.mnist.load_data
binary_crossentropy', optimizer=tf.keras.optimizers.Adam(0.0002, 0.5))# 加载MNIST数据集(x_train, _), (_, _) = tf.keras.datasets.mnist.load_data...+ kl_lossvae.add_loss(vae_loss)vae.compile(optimizer='adam')# 加载MNIST数据集(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data...pixelcnn.compile(optimizer='adam', loss='sparse_categorical_crossentropy')# 加载MNIST数据集(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data...input_shape)diffusion_model.compile(optimizer='adam', loss='mse')# 加载MNIST数据集(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data
Generator Loss: {g_loss}") # 主函数，加载数据并训练GAN模型 def main(): # 加载MNIST数据集作为示例 (X_train, _), (_, _) = tf.keras.datasets.mnist.load_data...return autoencoder # 主函数，加载数据并训练自编码器模型 def main(): # 加载MNIST数据集作为示例 (X_train, _), (X_test, _) = tf.keras.datasets.mnist.load_data
num_classes, activation='softmax') ]) return model# 加载数据集(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data
# 加载数据集(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
# 加载MNIST数据集(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()# 数据归一化x_train
import gradio, tensorflow as tf (x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data...函数中，如下所示： import tensorflow as tf import gradio n_classes = 10 (x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data
from_logits=True), metrics=['accuracy']) # 假设已有MNIST数据集 # (x_train, y_train), _ = tf.keras.datasets.mnist.load_data
layers import matplotlib.pyplot as plt 加载MNIST数据集并对其进行预处理： (x_train,y_train),(x_test,y_test) = tf.keras.datasets.mnist.load_data
tf.data.Dataset.from_tensor_slices() import matplotlib.pyplot as plt (train_data, train_label), (_, _) = tf.keras.datasets.mnist.load_data
编译模型autoencoder.compile(optimizer='adam', loss='binary_crossentropy')# 加载数据(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data
GAN模型def train_gan(generator, discriminator, epochs=10000, batch_size=128): (x_train, _), (_, _) = tf.keras.datasets.mnist.load_data
领取专属 10元无门槛券
手把手带您无忧上云
云服务器
ICP备案
实时音视频
对象存储
即时通信 IM
