What does tf mean? View the definition of tf and all related slang terms containing tf below: tf : The f**kUsage of TFThe abbreviation TF is commonly used as a short form of the phrase "The f**k." It is an exclamation used to express surprise, confusion, anger, or disbelief. ...
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(np_cropped_region_1.mean(), 1) self.assertEqual(np_cropped_region_1.std(), 0) self.assertAllEqual(np_cropped_region_2.shape, (21, 21, 50, 3)) # Check that the padded region is all zeros self.assertEqual(np_cropped_region_2[:5, :5, :, :].sum(),...
labels=tf.cast(tf.reshape(self._y, [-1, 1]), dtype=tf.float32))optimizer = tf.train.AdamOptimizer(self.learning_rate) predictions = tf.to_int32(tf.round(self.res)) correct_prediction = tf.equal(predictions, self._y) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32...
loss_mean = tf.reduce_mean(loss) train_op = tf.train.AdamOptimizer().minimize(loss) sess = tf.Session() init = tf.global_variables_initializer() sess.run(init) init = tf.local_variables_initializer() sess.run(init) 最后,我们通过调用tf.train.start_queue_runners()创建线程将数据入队到队列...
loss = tf.reduce_mean(loss)print("batch %d: loss %f"% (batch_index, loss.numpy())) grads = tape.gradient(loss, model.variables) optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables)) 交叉熵(cross entropy)与 tf.keras.losses ...
We're using Terraform's interpolation feature (variable) in the "aws_instance" resource where another resource is being referenced. key_name = "${aws_key_pair.terraform-demo.key_name}" Note that we useresource_type.logical_name.attribute!
4.根据所给出的音标写出句中所缺的单词(1)I like to chat/ion'lain/ and to meet newpeople in/tfaet/ rooms.(2)The college years are supposed to be a time for the/di'velapmant/ of adult/ar'dentati/.(3)The lawyer/'lisnd/ with full/a'tenfn/, trying not to miss any point.(4)...
loss=tf.reduce_mean(tf.square(y-y_data)) #因为有误差,所以建立一个神经网络,用神经网络优化误差,也就是误差优化器,减少误差 optimizer=tf.train.GradientDescentOptimizer(0.5)#0.5是learing rate,一般来说小于1 train=optimizer.minimize(loss) init=tf.initialize_all_variables()#初始化神经网络 ...
model.compile({optimizer:"sgd",loss:"meanSquaredError"}); 使用框架的一个巨大好处是,随着机器学习世界发明新的优化器如“Adagrad”和“Adamax”,只需在模型架构中简单更改一个字符串¹,就可以尝试并调用它们。将“sgd”切换为“adamax”对于开发人员来说几乎不需要时间,可能会显著提高模型训练时间,而无需阅读...