grads = tape.gradient(total_loss, self.trainable_weights) self.optimizer.apply_gradients(zip(grads, self.trainable_weights)) self.total_loss_tracker.update_state(total_loss) self.reconstruction_loss_tracker.update_state(reconstruction_loss) self.kl_loss_tracker.update_state(kl_loss) return { "loss...
这次的学习目标就是模型构建的一些相关 API,其中模型的构建,包括 Model 和 layers,然后我们模型的损失函数、优化器、损失等等,主要包括 losses、optimizer、metrics。其中这个 optimizer 呢,之前我们刚刚接触过,已经讲解过了。 接着,我们来看看「模型构建」,我们在 Tensorflow 当中推荐使用Keras来构建模型,它是一个广为...
创建loss和optimizer进行训练 在上一步中,我们已经创建好了model,接下来就要创建loss和optimizer进行训练 compile方式 首先定义好loss、optimizer、以及需要监控的指标作为compile的参数 调用fit或者fit_generate进行训练 model.compile(loss=losses.BinaryCrossentropy(from_logits=True),optimizer='adam',metrics=tf.metrics....
optimizer = tf.keras.optimizers.Adam(lrate) zero_grads = [tf.zeros_like(w) for w in grad_vars] # Apply gradients which don't do nothing with Adam optimizer.apply_gradients(zip(zero_grads, grad_vars)) # Set the weights of the optimizer optimizer.set_weights(opt_weights) # NOW set th...
with tf.GradientTape()as tape:y_pred=self(x,training=True)# Forward pass# Compute our own lossloss=keras.losses.mean_squared_error(y,y_pred)# Compute gradientstrainable_vars=self.trainable_variables gradients=tape.gradient(loss,trainable_vars)# Update weightsself.optimizer.apply_gradients(zip(grad...
optimizer.apply_gradients(zip(gradients, model.trainable_variables)) train_loss(loss) train_accuracy(labels, predictions) # define test function including calculating loss and calculating accuracy @tf.function def test_step(images, labels): predictions = model(images) ...
apply_gradients(zip(gradient_l, mmodel.trainable_variables)) def train(): mmodel = Map_model(is_train=True) optimizer = tf.keras.optimizers.Adam(0.01) loss_object = tf.keras.losses.CategoricalCrossentropy() train_loss = tf.keras.metrics.Mean(name='train_loss') train_accuracy = tf.keras...
optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables)) print(model.variables) 这里,我们没有显式地声明a和b两个变量并写出y_pred = a * X + b这一线性变换,而是建立一个继承tf.keras.Model的模型类Linear: 这个类在初始化部分实例化了一个 全连接层(tf.keras.layers.Dense), ...
pred_min = self.p_optimizer.apply_gradients(pred_grad) return {"pred_loss": pred_loss, "adv_loss": adv_loss} def test_step(self, data): # Pass tf.data.Dataset element based on numpy arrays x, y_true, z_true = data # Compute predictions for Predictor ...
elifself.hps.optimizer=='mom': optimizer=tf.train.MomentumOptimizer(self.lrn_rate,0.9) # 梯度优化操作-->合并BN更新操作-->建立优化操作组 apply_op=optimizer.apply_gradients( zip(grads,trainable_variables), global_step=self.global_step,