print('# Assistant Response 1:') responses = [] for responses in llm.chat(messages=messages, functions=functions, stream=True): print(responses) messages.extend(responses) # extend conversation with assistant's function_response, }) # extend conversation with function response print('# Assistant Response 2:') for responses in llm.chat( (续下页) 1.13. Function Calling 39 Qwen (接上页) messages=messages, functions=functions stream=True, ): # get a new response from the model where it can see the function response print(responses) if __name__ == '__main__': test() 1.14 Qwen-Agent Qwen-Agent 是一个基于 Qwen 的指令跟随、工具使用、计划和记忆能力来开发

where we expect exact optimal answers, machine learning applications can often tolerate approximate responses, since often there are no exact answers. Machine learning algorithms help build models, which as

number of precision bits to all subranges of the same length. In this scenario, the dequantization error would be large for ranges where the data is densely distributed. Quantization-aware training can mitigate losses by making the network resilient to the errors, but if we want to minimize the dequantization error, this is not an ideal situation. Refer to figure 5-6 for an illustration of this problem. 13 Kurtz elements. For a moment, let’s assume that the centroids we obtain are optimal, i.e. the reconstruction error when we decode the encoded representation is minimal14. In such a scenario, we can list all the centroids

all the modules, it will add everything and if anything went wrong, you will get module not found error message. Keras 9 This chapter explains Keras backend implementations TensorFlow represent the data format.  epsilon represents numeric constant. It is used to avoid DivideByZero error.  floatx represent the default data type float32. You can also change it to float16 or float64 function and Optimizer are used in learning phase to find the error (deviation from actual output) and do optimization so that the error will be minimized.  Fit the model: The actual learning

4 � = �3 8 � 12 / 19 Fundamental Equations • The error in the j-th neuron in the l-th layer: δ[l] j = ∂L ∂z[l] j • An equation for the error in the output layer δ[L] j = ∂L ∂a[L] j σ′(z[L] j ) Fundamental Equations • An equation for the error δl in terms of the error in the next layer δl+1 δ[l] = ((w[l+1])T δ[l+1]) ⊙ σ′(z[l]) • (w[l+1])T δ[l+1] is to move the error backward through the networks, giving of measure of the error at the output of the l-th layer • ((w[l+1])T δ[l+1]) ⊙ σ′(z[l]) is to move the error backward through the activation function in the layer l, giving us the error δl in the weight

authors report needing fewer labeled examples. Refer to figure 6-6 for a comparison between the error obtained by training from scratch v/s using pre-training strategies. 5 WikiText-103 dataset is derived Text Classification." arXiv, 18 Jan. 2018, doi:10.48550/arXiv.1801.06146. Figure 6-6: Validation error w.r.t. number of training examples for different training methods on IMDb (from scratch, ULMFiT supervised: would be easier to learn. The authors report a statistically significant difference between the final error in the word prediction curriculum and non-curriculum strategies. How do we go about implementing

. . . . . . . . . . . . . 134 7.2.1 mean_squared_error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 7.2.2 mean_absolute_error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 7.2.3 mean_absolute_percentage_error . . . . . . . . . . . . . . . . . . . . . . . . 134 7.2.4 mean_squared_logarithmic_error . . . . . . . . . . . . . . . . . . . . . . . 134 7.2.5 损失函数（或称目标函数、优化评分函数）是编译模型时所需的两个参数之一： model.compile(loss='mean_squared_error', optimizer='sgd') from keras import losses model.compile(loss=losses.mean_squared_error, optimizer='sgd') 你可以传递一个现有的损失函数名，或者一个 TensorFlow/Theano

and thus save some costs? What if it did not matter to us if x was stored/transmitted with some error (-5.023 v/s -5.0)? If we can tolerate some loss of precision, can we use b-bits and save some space dequantized array upon running the code. Note that the last element was supposed to be 10.0, and the error is 2.5. array([-10. , -7.5, -5. , -2.5, 0. , 2.5, 5. , 7.5, 7.5]) So far so good! We the two weights using the Root Mean Square Error (RMSE), which gives an idea of the amount of information lost in the quantization process. As we see, the error is quite small. weights_dequantized = de

the training data as closely as possible. To measure the error between h✓ and the training data, we define a cost function (also called error function) J(✓) : Rn+1 ! R as follows J(✓) = 1 2 m X i=1 optimization method. In each iteration, the parameters are updated according to the gra- dient of the error (i.e., the cost function) with respect to one training sample only. Hence, it entails very limited

均方误差（Mean Square Error,MSE） MSE = 1 ? ෍ ?=1 ? ( ?(?) − ෝ?(?))2 均方根误差 RMSE(Root Mean Square Error,RMSE) RMSE(?, ෝ?) = 1 ? ෍ ?=1 ? ( ?(?) − ෝ?(?))2 平均绝对误差（Mean Absolute Error,MAE） MAE(?, ෝ?)