loss = hinge_loss + l2_loss op = tf.train.AdamOptimizer(lr).minimize(loss) y_hat = tf.to_double(z > 0) - tf.to_double(z <= 0) acc = tf.reduce_mean(tf.to_double(tf.equal(y_hat, y))) 1. 2. 3. 4. 5. 6. 7. 使用训练集训练模型。 losses = [] accs = [] with tf...
def inference(dataset_loader,net,criterion,optimizer): import math loss_history = [] cur_loss = 0 if not os.path.exists(model_dir): os.makedirs(model_dir) if pretrain: ckpt = torch.load(premodelpath) net.load_state_dict(ckpt['model_state_dict']) optimizer.load_state_dict(ckpt['optim...
op=tf.train.AdamOptimizer(lr).minimize(loss) 定义度量指标。我们在测试集上计算它,为此,我们在计算图中定义测试集。 x_test=tf.placeholder(tf.float64,[None,n_input])y_test=tf.placeholder(tf.float64,[None,1])kernel_pred=rbf_kernel(x_train,x_test,gamma)y_hat=tf.transpose(kernel_pred)@(y_...
AdamOptimizer是目前 TF 中最好的优化器。我们一开始就是用这个优化器,可以避免很多坑。 loss = tf.reduce_mean((z - y) ** 2)op = tf.train.AdamOptimizer(lr).minimize(loss)y_mean = tf.reduce_mean(y)r_sqr = 1 - tf.reduce_sum((y - z) ** 2) / tf.reduce_sum((y - y_mean) **...
opt = Adam(lr=0.0002, beta_1=0.5) model.compile(loss='binary_crossentropy', optimizer=opt, loss_weights=[0.5]) return model # define image shape image_shape = (256,256,3) # create the model model = define_discriminator(image_shape) # summarize the model model.summary() # plot the ...
if optimization == 'adam': optimizer = Adam(learning_rate=lr_schedule) if optimization == 'SGD': optimizer = SGD(learning_rate=lr_schedule) model.compile( optimizer=optimizer, loss=LOSS_FUNC, metrics=[METRIC]) return model Fitness is the function that skopt will optimize with ‘gp_minimize...
5. AdamW Optimizer The AdamW is another version of Adam optimizer algorithms and basically, it is used to perform optimization of both weight decay and learning rate. One more advantage of the optimizer is that it is faster. 6. Adamax ...
model.compile(loss='mse', optimizer=Adam(lr=0.0002, beta_1=0.5), loss_weights=[0.5]) return model # define image shape image_shape = (256,256,3) # create the model model = define_discriminator(image_shape) # summarize the model model.summary() # plot the model plot_model(model, to...
(x) # Create the fine-tuned model model = Model(inputs=base_model.input, outputs=output) # Compile the model model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy']) # Fine-tune on skin lesion dataset history = model.fit(train_generator, epochs=10,...
op = tf.train.AdamOptimizer(lr).minimize(loss) y_mean = tf.reduce_mean(y) r_sqr =1- tf.reduce_sum((y - h_l2) **2) / tf.reduce_sum((y - y_mean) **2) 使用训练集训练模型。 losses = [] r_sqrs = []withtf.Session()assess: ...