def cnn(rate, convDim, dim, optimizer): model = Sequential() model.add(Conv2D(2 * convDim, (5, 5), strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(2 * convDim, (3, 3), strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(rate)) model.add(Conv2D(convDim, (3, 3), strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(2 * convDim, (2, 2), strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(rate)) model.add(Flatten()) model.add(Dense(units=dim, kernel_initializer='uniform', activation='relu')) model.add(Dropout(rate / 2)) model.add(Dense(units=2 * dim, kernel_initializer='uniform', activation='relu')) model.add(Dropout(rate / 2)) model.add(Dense(units=2 * dim, kernel_initializer='uniform', activation='relu')) # model.add(Dense(256, kernel_initializer = 'uniform', activation='softmax')) model.add(Dense(7, kernel_initializer='uniform', activation='softmax')) ###############################改组数 model.compile(optimizer, loss='categorical_crossentropy', metrics=['accuracy']) return model 减少这个模型的神经元数量

PyTorch中model.zero_grad()和optimizer.zero_grad()用法

主要介绍了PyTorch中model.zero_grad()和optimizer.zero_grad()用法，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

TypeError: Unexpected keyword argument passed to optimizer: learning_rate 解决方法

TypeError: Unexpected keyword argument passed to optimizer: learning_rate 3.问题定位：先看报错代码：大概意思是，传给优化器的learning_rate参数错误。模型训练是在服务器Linux环境下进行的，之后在...

Linq.Expression.Optimizer：System.Linq.Expression表达式优化器。 http：thorium.github.ioLinq.Expression.Optimizer

def build_generator(latent_dim): model = tf.keras.Sequential() model.add(Dense(7 * 7 * 256, input_dim=latent_dim)) model.add(LeakyReLU(alpha=0.2)) model.add(Reshape((7, 7, 256))) model.add(Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same')) model.add(LeakyReLU(alpha=0.2)) model.add(Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same')) model.add(LeakyReLU(alpha=0.2)) model.add(Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', activation='tanh')) return model # 定义判别器 def build_discriminator(input_shape): model = tf.keras.Sequential() model.add(Conv2D(64, (5, 5), strides=(2, 2), padding='same', input_shape=input_shape)) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.3)) model.add(Conv2D(128, (5, 5), strides=(2, 2), padding='same')) model.add(LeakyReLU(alpha=0.2)) model.add(Dropout(0.3)) model.add(Flatten()) model.add(Dense(1, activation='sigmoid')) return model # 定义GAN模型 def build_gan(generator, discriminator): discriminator.trainable = False model = tf.keras.Sequential() model.add(generator) model.add(discriminator) return model # 定义损失函数和优化器 loss_fn = BinaryCrossentropy() generator_optimizer = Adam(learning_rate=0.0002, beta_1=0.5) discriminator_optimizer = Adam(learning_rate=0.0002, beta_1=0.5)

生成器采用了反卷积（Conv2DTranspose）来进行图像的生成，判别器采用了卷积神经网络来对生成的图像进行判别。GAN模型将生成器和判别器串联在一起，生成器的输出作为判别器的输入，整个模型的目标是通过训练生成器来...

model.compile(optimizer=tf.keras.optimizers.Adam(***), ^ SyntaxError: invalid syntax

model.add(Dense(10, input_shape=(5,), activation='relu')) model.add(Dense(1, activation='sigmoid')) # 编译模型 optimizer = tf.keras.optimizers.Adam(learning_rate=0.001) model.compile(optimizer=...

往下面这个模型加入损失函数 # 自定义卷积图像分类模型 def cnn_classify(rate, convDim, dim, optimizer): model = Sequential() # 用于提取图像的特征 model.add(Conv2D(2 * convDim, (5, 5), strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(2 * convDim, (3, 3), strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(rate)) # 用于进一步提取图像的特征 model.add(Conv2D(convDim, (3, 3), strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(2 * convDim, (2, 2), strides=1, activation='relu', padding='same')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(rate)) # Flatten层，将输出展平成一维数组 model.add(Flatten()) # 全连接层，进行特征分类 model.add(Dense(units=dim / 2, kernel_initializer='uniform', activation='relu')) model.add(Dropout(rate / 2)) model.add(Dense(units=dim, kernel_initializer='uniform', activation='relu')) model.add(Dropout(rate / 2)) model.add(Dense(units=dim, kernel_initializer='uniform', activation='relu')) # model.add(Dense(256, kernel_initializer = 'uniform', activation='softmax')) model.add(Dense(7, kernel_initializer='uniform', activation='softmax')) model.compile(optimizer, loss='categorical_crossentropy', metrics=['accuracy']) return model

在该模型中，已经加入了损失函数为交叉熵损失函数，可以通过 model.compile(optimizer, loss='categorical_crossentropy', metrics=['accuracy']) 来设置损失函数和优化器。如果想使用其他的损失函数，可以将 ...

def create_model(): model = Sequential() model.add(LSTM(50, input_shape=(train_x.shape[1], train_x.shape[2]))) model.add(Dense(1)) model.compile(loss='mae', optimizer='adam') model.summary() return model model = create_model()

它使用了Keras库中的Sequential模型，并添加了一个LSTM层和一个Dense层。LSTM层的参数为50，输入形状为(train_x.shape[1], train_x.shape[2])，其中train_x是输入数据的训练集。Dense层的输出为1。模型使用均方误差...

下面代码在tensorflow中出现了init() missing 1 required positional argument: 'cell'报错： class Model(): def init(self): self.img_seq_shape=(10,128,128,3) self.img_shape=(128,128,3) self.train_img=dataset # self.test_img=dataset_T patch = int(128 / 2 ** 4) self.disc_patch = (patch, patch, 1) self.optimizer=tf.keras.optimizers.Adam(learning_rate=0.001) self.build_generator=self.build_generator() self.build_discriminator=self.build_discriminator() self.build_discriminator.compile(loss='binary_crossentropy', optimizer=self.optimizer, metrics=['accuracy']) self.build_generator.compile(loss='binary_crossentropy', optimizer=self.optimizer) img_seq_A = Input(shape=(10,128,128,3)) #输入图片 img_B = Input(shape=self.img_shape) #目标图片 fake_B = self.build_generator(img_seq_A) #生成的伪目标图片 self.build_discriminator.trainable = False valid = self.build_discriminator([img_seq_A, fake_B]) self.combined = tf.keras.models.Model([img_seq_A, img_B], [valid, fake_B]) self.combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1, 100], optimizer=self.optimizer,metrics=['accuracy']) def build_generator(self): def res_net(inputs, filters): x = inputs net = conv2d(x, filters // 2, (1, 1), 1) net = conv2d(net, filters, (3, 3), 1) net = net + x # net=tf.keras.layers.LeakyReLU(0.2)(net) return net def conv2d(inputs, filters, kernel_size, strides): x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.LeakyReLU(alpha=0.2)(x) return x d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3)) out= ConvRNN2D(filters=32, kernel_size=3,padding='same')(d0) out=tf.keras.layers.Conv2D(3,1,1,'same')(out) return keras.Model(inputs=d0, outputs=out) def build_discriminator(self): def d_layer(layer_input, filters, f_size=4, bn=True): d = tf.keras.layers.Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input) if bn: d = tf.keras.layers.BatchNormalization(momentum=0.8)(d) d = tf.keras.layers.LeakyReLU(alpha=0.2)(d) return d img_A = tf.keras.layers.Input(shape=(10, 128, 128, 3)) img_B = tf.keras.layers.Input(shape=(128, 128, 3)) df = 32 lstm_out = ConvRNN2D(filters=df, kernel_size=4, padding="same")(img_A) lstm_out = tf.keras.layers.LeakyReLU(alpha=0.2)(lstm_out) combined_imgs = tf.keras.layers.Concatenate(axis=-1)([lstm_out, img_B]) d1 = d_layer(combined_imgs, df)#64 d2 = d_layer(d1, df * 2)#32 d3 = d_layer(d2, df * 4)#16 d4 = d_layer(d3, df * 8)#8 validity = tf.keras.layers.Conv2D(1, kernel_size=4, strides=1, padding='same')(d4) return tf.keras.Model([img_A, img_B], validity)

你在定义 Model 类时，没有传入参数 cell，但是在代码中使用了 ConvRNN2D 的实例化对象，这个对象需要一个 cell 参数。你需要在初始化函数中添加这个参数，如下所示： class Model(): def __init__(self, cell...

import tensorflow as tf from tensorflow import keras from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten from tensorflow.keras.layers import Conv2D, Conv1D, BatchNormalization, MaxPooling2D,Conv2DTranspose from tensorflow.keras.optimizers import Adam # 优化器 import tensorflow.keras from tensorflow.keras import optimizers def build_model(): model = Sequential() # Sequential模型是keras两种模型之一，另一种是model模型 """构建模型""" # 第一层卷积，需要指定input_shape的参数 num_classes = 7 img_size = 48 model.add(Conv2D(32, (1, 1), strides=1, padding='same', input_shape=(img_size, img_size, 1))) model.add(Activation('relu')) # 激活函数 model.add(Conv2D(32, (5, 5), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) # 最大池化 model.add(Conv2D(32, (3, 3), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, (5, 5), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(2048)) # 全连接层 model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(1024)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer=optimizers.RMSprop(learning_rate=0.0001), metrics=['accuracy']) # 自动扩充训练样本 model.summary() # 显示训练模型结构 return model 帮我写注释

model.add(Conv2D(32, (5, 5), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) # 最大池化 model.add(Conv2D(32, (3, 3), padding='same')) model.add(Activation...

使用paddle将以下LeNet代码进行模型优化class LeNet(paddle.nn.Layer): def init(self): super(LeNet, self).init() # 创建卷积和池化层块，每个卷积层使用relu激活函数，后面跟着一个2x2的池化 self.conv1 = paddle.nn.Conv2D(3, 32, 3, 1, 1) self.relu1 = paddle.nn.ReLU() self.max_pool1 = paddle.nn.MaxPool2D(2, 2) self.conv2 = paddle.nn.Conv2D(32, 64, 3, 1, 1) self.relu2 = paddle.nn.ReLU() self.max_pool2 = paddle.nn.MaxPool2D(2, 2) self.avg_pool = AdaptiveAvgPool2D(1) self.linear= paddle.nn.Linear(64, 2) # 网络的前向计算过程 def forward(self, x): x = self.max_pool1(self.relu1(self.conv1(x))) x = self.max_pool2(self.relu2(self.conv2(x))) x = self.avg_pool(x) x = paddle.reshape(x, [x.shape[0],-1]) x = self.linear(x) return x paddle.Model(LeNet()).summary((-1,3,256,256))

optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()) # 创建模型实例 model = paddle.Model(LeNet()) # 编译模型 model.prepare(optimizer=optimizer, loss=paddle.nn.Cross...

def build_model(self, input_shape, nb_classes): x = keras.layers.Input(input_shape) conv1 = keras.layers.Conv1D(128, 8, 1, padding='same')(x) conv1 = keras.layers.BatchNormalization()(conv1) conv1 = keras.layers.Activation('relu')(conv1) conv2 = keras.layers.Conv1D(256, 5, 1, padding='same')(conv1) conv2 = keras.layers.BatchNormalization()(conv2) conv2 = keras.layers.Activation('relu')(conv2) conv3 = keras.layers.Conv1D(128, 3, 1, padding='same')(conv2) conv3 = keras.layers.BatchNormalization()(conv3) conv3 = keras.layers.Activation('relu')(conv3) full = keras.layers.GlobalAveragePooling1D()(conv3) out = keras.layers.Dense(nb_classes, activation='softmax')(full) model = keras.models.Model(inputs=x, outputs=out) # optimizer = keras.optimizers.Adam() optimizer = keras.optimizers.Nadam() model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) return model

这是在 Keras 框架中定义一个模型的代码。这个模型由输入层、三个卷积层、全局平均池化层、全连接层和输出层组成。卷积层之间还有批归一化层和激活层。这个模型的输入尺寸为 input_shape ，输出的类别数为 nb_...

def CNN_LSTM_model(input_size): model = Sequential() model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(input_size, 1))) model.add(Conv1D(filters=64, kernel_size=3, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Dropout(0.5)) model.add(Conv1D(filters=128, kernel_size=3, activation='relu')) model.add(Conv1D(filters=128, kernel_size=3, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Dropout(0.5)) model.add(Conv1D(filters=256, kernel_size=3, activation='relu')) model.add(Conv1D(filters=256, kernel_size=3, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Dropout(0.5)) model.add(LSTM(64, return_sequences=True)) model.add(LSTM(32)) model.add(Dense(10, activation='relu')) model.add(Dense(1, activation='sigmoid')) return model改写成更适合5g异常流量数据分析的代码

def CNN_LSTM_model(input_size): model = Sequential() model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(input_size, 3))) model.add(Conv1D(filters=64, kernel_size=3, ...

model=tf.keras.models.sequential() AttributeError: module 'tensorflow.keras.models' has no attribute 'sequential'

model.add(tf.keras.layers.Dense(64, activation='relu', input_shape=(input_dim,))) model.add(tf.keras.layers.Dense(10, activation='softmax')) # 编译模型 model.compile(optimizer='adam', loss='...

验证码训练数据为图片，标签为图片中的数字下述代码进行修正import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense # 构建模型 model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Flatten()) model.add(Dense(64, activation='relu')) model.add(Dense(36, activation='softmax')) # 36表示0-9数字和A-Z字母的类别数 # 编译模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # 训练模型 train_data = [...] # 加载训练数据 train_labels = [...] # 加载训练标签 model.fit(train_data, train_labels, epochs=10, batch_size=32) # 保存模型 model.save('captcha_model.h5')

model.add(MaxPooling2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Flatten()) model.add(Dense(64, activation='relu')) model.add(Dense(36, ...

def model(self): num_classes = self.config.get("CNN_training_rule", "num_classes") seq_length = self.config.get("CNN_training_rule", "seq_length") conv1_num_filters = self.config.get("CNN_training_rule", "conv1_num_filters") conv1_kernel_size = self.config.get("CNN_training_rule", "conv1_kernel_size") conv2_num_filters = self.config.get("CNN_training_rule", "conv2_num_filters") conv2_kernel_size = self.config.get("CNN_training_rule", "conv2_kernel_size") hidden_dim = self.config.get("CNN_training_rule", "hidden_dim") dropout_keep_prob = self.config.get("CNN_training_rule", "dropout_keep_prob") model_input = keras.layers.Input((seq_length,1), dtype='float64') # conv1形状[batch_size, seq_length, conv1_num_filters] conv_1 = keras.layers.Conv1D(conv1_num_filters, conv1_kernel_size, padding="SAME")(model_input) conv_2 = keras.layers.Conv1D(conv2_num_filters, conv2_kernel_size, padding="SAME")(conv_1) max_poolinged = keras.layers.GlobalMaxPool1D()(conv_2) full_connect = keras.layers.Dense(hidden_dim)(max_poolinged) droped = keras.layers.Dropout(dropout_keep_prob)(full_connect) relued = keras.layers.ReLU()(droped) model_output = keras.layers.Dense(num_classes, activation="softmax")(relued) model = keras.models.Model(inputs=model_input, outputs=model_output) # model.compile(loss="categorical_crossentropy", # optimizer="adam", # metrics=["accuracy"]) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) print(model.summary()) return model给这段代码每行加上注释

conv_2 = keras.layers.Conv1D(conv2_num_filters, conv2_kernel_size, padding="SAME")(conv_1) # 全局最大池化 max_poolinged = keras.layers.GlobalMaxPool1D()(conv_2) # 全连接层 full_connect = keras....

将下面代码使用ConvRNN2D层来替换ConvLSTM2D层，并在模块init.py中创建类‘convrnn’ class Model(): def init(self): self.img_seq_shape=(10,128,128,3) self.img_shape=(128,128,3) self.train_img=dataset # self.test_img=dataset_T patch = int(128 / 2 ** 4) self.disc_patch = (patch, patch, 1) self.optimizer=tf.keras.optimizers.Adam(learning_rate=0.001) self.build_generator=self.build_generator() self.build_discriminator=self.build_discriminator() self.build_discriminator.compile(loss='binary_crossentropy', optimizer=self.optimizer, metrics=['accuracy']) self.build_generator.compile(loss='binary_crossentropy', optimizer=self.optimizer) img_seq_A = Input(shape=(10,128,128,3)) #输入图片 img_B = Input(shape=self.img_shape) #目标图片 fake_B = self.build_generator(img_seq_A) #生成的伪目标图片 self.build_discriminator.trainable = False valid = self.build_discriminator([img_seq_A, fake_B]) self.combined = tf.keras.models.Model([img_seq_A, img_B], [valid, fake_B]) self.combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1, 100], optimizer=self.optimizer,metrics=['accuracy']) def build_generator(self): def res_net(inputs, filters): x = inputs net = conv2d(x, filters // 2, (1, 1), 1) net = conv2d(net, filters, (3, 3), 1) net = net + x # net=tf.keras.layers.LeakyReLU(0.2)(net) return net def conv2d(inputs, filters, kernel_size, strides): x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.LeakyReLU(alpha=0.2)(x) return x d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3)) out= tf.keras.layers.ConvRNN2D(filters=32, kernel_size=3,padding='same')(d0) out=tf.keras.layers.Conv2D(3,1,1,'same')(out) return keras.Model(inputs=d0, outputs=out)

from tensorflow.keras.layers import Conv2D, Conv2DTranspose, ConvRNN2D, Input, BatchNormalization, LeakyReLU, Flatten, Dense, Reshape from tensorflow.keras.models import Model class Model(): def __...

model = Sequential() model.add(LSTM(10, activation = "tanh",input_shape=(window_size, 1))) model.add(Dense(1)) model.add(tfp.layers.DistributionLambda( lambda t: tfd.Normal(loc=t[...,0], scale=0.01*tf.math.softplus(t[...,1])), name='normal_dist')) # note this negloglik = lambda y, p_y: -p_y.log_prob(y) # note this opt = keras.optimizers.Adam(learning_rate=0.001) model.compile(loss=negloglik, optimizer= opt) #model.compile(loss="mse", optimizer= opt)中，改为model.add(Dense(2))，测试集中的MAPE上升，怎么办

当您将 model.add(Dense(1)) 更改为 model.add(Dense(2)) 后，您的模型将输出两个值，一个是均值，另一个是标准差。这表明您希望使用正态分布来拟合您的目标。但您需要相应地更改损失函数，使其适用于正态分布。...

def init_optimizer(self, **kwargs): # Choose optimizer model = self.model_container.models['model'] try: opt_type = self.cfg.optimizer freeze = getattr(self.cfg, 'freeze', False) or getattr(self.cfg, 'train_classifier', False) if opt_type == 'SGD': print('Using SGD as optimizer') if freeze: print('Freezing weights!') self.optimizer = optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=self.cfg.learning_rate, momentum=self.cfg.momentum, weight_decay=self.cfg.weight_decay) else: self.optimizer = optim.SGD(model.parameters(), lr=self.cfg.learning_rate, momentum=self.cfg.momentum, weight_decay=self.cfg.weight_decay) elif opt_type == 'Adam': print('Using Adam as optimizer') if freeze: print('Freezing weights!') self.optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=self.cfg.learning_rate, weight_decay=self.cfg.weight_decay) else: self.optimizer = optim.Adam(model.parameters(), lr=self.cfg.learning_rate, weight_decay=self.cfg.weight_decay) except AttributeError: self.optimizer = optim.SGD(model.parameters(), lr=self.cfg.learning_rate, momentum=self.cfg.momentum, weight_decay=self.cfg.weight_decay)这个函数什么意思

这个函数是一个初始化优化器的函数，它的作用是根据配置文件中的参数选择使用哪种优化器（SGD或Adam），并根据需要决定是否冻结模型权重（freeze），以及设置相应的超参数（学习率、动量、权重衰减等）。...

js-framerate-optimizer:用于随时间跟踪和迭代提高页面帧率的库

js-framerate-optimizer 受启发的库，用于随着时间的推移跟踪和迭代地提高帧速率。术语“增加工作”和“减少工作”分别是“改善质量”和“改善性能”的同义词。用 import { Optimizer } from '.../Optimizer.js'...

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Linq.Expression.Optimizer：System.Linq.Expression表达式优化器。 http：thorium.github.ioLinq.Expression.Optimizer

model.compile(optimizer=tf.keras.optimizers.Adam(***), ^ SyntaxError: invalid syntax

def create_model(): model = Sequential() model.add(LSTM(50, input_shape=(train_x.shape[1], train_x.shape[2]))) model.add(Dense(1)) model.compile(loss='mae', optimizer='adam') model.summary() return model model = create_model()

model=tf.keras.models.sequential() AttributeError: module 'tensorflow.keras.models' has no attribute 'sequential'

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