deep learning tutorial python

First the PO on the main Python code (2.7), this code can be found on the deep learning. 1 # Allocate symbolic variables for the data 2 index = T.lscalar () # Index to a [mini]batch 3 x = T.matrix (' x ') # The data is presented as rasterized images 4 y = t.ivector (' y ') # The labels is presented as 1D vector of 5 # [INT] Labels 6 7 #

) Percent STEP 6:testnumcases = Size (data, 2);d epth = Numel (stack); z = cell (depth+1, 1); % Pitchfork name Mitsu + 闅 Refer bookmark ba kinh crypto za = cell (depth+1, 1); % Fork name Mitsu + 闅 Refer bookmark ba kinh crypto upsome} = credential i = 暟 % a{1 $ data;for 1:depth optin 畻 闅 refer bookmark z ba kinh crypto tel 屾 縺} = animals * Tapes} + z{i+1 (STACK{I}.W, 1, numcases); A{i+1} = sigmoid (z{i+1}); end[~, pred] = max (Softmaxtheta * a{depth+1});in the end I

Python Learning Notes (children under five)deep copy-shallow copy a shallow copy is a copy of the reference (copying only the parent object) a deep copy is a resource copy of an object. normal replication simply adds a "label" to the same address space,

layer still uses the original memory location.With a deep copy, a new dictionary is generated, and the dictionary's ID value is different, and the key in the dictionary generates a new copy, but the ID of the key in the dictionary is still the same. What's the difference? In fact, the difference between a dark copy is the level of the copy, the shallow copy only copies the first layer, and the deep copy co

. You'll need to the know how-to-use this functions for future assignments. 1.1-sigmoid function, Np.exp () Before using Np.exp (), you'll use MATH.EXP () to implement the Sigmoid function. You'll then why Np.exp () is preferable to Math.exp (). Exercise: Build a function that returns the sigmoid's a real number X. Use MATH.EXP (x) for the exponential funct Ion. Reminder:Sigmoid (x) =11+e−x sigmoid (x) = \frac{1}{1+e^{-x} is sometimes also known as the The logistic function. It is a non-linear f

():... "" does nothing, but document it....... No, really, it doesn ' t do anything.... “””... pass...Print (my_function. Doc)Do nothing, but document it.No, really, it doesn‘t do anything.function commentfunction annotations are completely optional, arbitrary metadata information for user-defined functions. Whether it is python itself or using standard library function annotations in any way, this section shows only the syntax. Third party items ar

","Luby"],"Hongpingshui","Guochaoxi"] theNames2=copy.deepcopy (names) -names[3][0]="SHOW530" -names[3][1]="Luby" - Print(Names2) + -Output Results >>>>>>> + A['Hongtao','Xiaoweihong','Hongyuchan', ['show530','Luby'],'Hongpingshui','Guochaoxi']4. The position representation method and interval of the elements in the listNames[0:-1] Represents the first element from a list names to the last element;Names[0:-1:2] Represents the first element from the list names to the last element, with a step of

Learning means finding a set of weights on the training data to minimize the loss function; Learning process: Calculates the gradient value of the loss function corresponding to the weight coefficient in the small batch data, then the weight coefficient moves along the gradient in the opposite direction; The probability of the learning process i

= [Np.zeros (w.shape) for W in Self.weights]Delta_b = [Np.zeros (b.shape) for B in self.biases]For x, y in Mini_batch:(Here for all samples in a small batch, apply reverse propagation, accumulate weights and bias changes)delta_w_p, delta_b_p = Self.backprop (x, y)Delta_w = [Dt_w + dt_w_p for dt_w,dt_w_p in Zip (Delta_w, delta_w_p)]Delta_b = [Dt_b + dt_b_p for dt_b,dt_b_p in Zip (Delta_b, delta_b_p)]Self.weights = [W (Eta/len (Mini_batch) *NW) for W,NW in Zip (Self.weights, delta_w)]Self.biases

training set, and its information about the data comes from the discriminator. Gan is difficult to train because the training of Gan is a dynamic process , rather than a simple gradient descent process with a fixed loss. Gan proper training requires some heuristic techniques, as well as a large number of parameter adjustments. Gan can produce highly realistic images. However, unlike VAE, the potential space they learn does not have a neat continuous structure , and therefore may not be

,input_dim=2)) Model.add (Activation (' Relu ')) Add elements of the model in turn dense layer (fully connected layer): mainly defines the main structure of input, output and hidden layer of the model. Dense (12,input_dim=2) is a hidden layer of 12 nodes, the input layer is 2 nodes, and the input layer must be the second parameter. activation function (Activation): can be self-contained in the Keras library, or it can be customized objective function (loss function):

objects.Deep copy:Names2 = copy.deepcopy (names) 12345678910111213141516 >>>importcopy>>> alist=[1,2,3,["a","b"]]>>> d=copy.deepcopy(alist)>>>print(alist);print(d)[1,2,3, [‘a‘,‘b‘]][1,2,3, [‘a‘,‘b‘]]始终没有改变>>> alist.append(5)>>>print(alist);print(d)[1,2,3, [‘a‘,‘b‘],5][1,2,3, [‘a‘,‘b‘]]始终没有改变>>> alist[3][‘a‘,‘b‘]>>> alist[3].append("ccccc")>>>print(alist);print(d)[1,2,3, [‘a‘,‘b‘,‘ccccc‘],5][1,2,3, [‘a‘,‘b‘]] 始终没有改变 Python

= copy.deepcopy(names)>>> Import copy>>> alist=[1,2,3,["A", "B"]]>>> d=copy.deepcopy (alist) >>> print ( alist);p rint (d) [1, 2, 3, [' A ', ' B ']][1, 2, 3, [' A ', ' B ']] always unchanged >>> alist.append (5) >>> print (alist); Print (d) [1, 2, 3, [' A ', ' B '], 5][1, 2, 3, [' A ', ' B ']] never changed >>> alist[3][' A ', ' B ']>>> alist[3].append ("cc CCC ") >>> print (alist);p rint (d) [1, 2, 3, [' A ', ' B ', ' CCCCC '], 5][1, 2, 3, [' A ', ' B ']] never changedPython

The goal of this blog is to introduce the introduction of torch Bloggers use the Itorch interface to write, the following images to show the code.If you can't remember the name of the method can be in the Itorch Point "tab" key will have intelligent input, similar to MATLAB Simple Introduction to String,numbers,tables The action of the string is a single quotation mark, and then the print () function in the second row is a bit like the cout in C + +, which can be displayed accord

Use of functionsThis is the definition of the function, the declaration of the keyword + defined function name + the name of the formal parameter, the blogger returns two values, the function of the specific functions in the back againThis is the initialization of a 5x2 matrix, and the initial value is 1. Here's a way to initialize the matrix.This is to declare a 2x5 matrix before calling the fill () method whose values are all initialized to 4.Input a A A, a matrix into the addtensors function,

function and a macro Macro inline functions Processing mode Processed by the preprocessor, just for simple text substitution Handled by the compiler, the body of the function is embedded in the calling place. But inline requests can also be rejected by the compiler Type check Do not do type checking Features that have normal functions are checked for parameters and return types. Side effects Yes

About the dictionary copy function dict.copy () actually applying replication issues#-*-Coding:utf-8-*-ab={' 1 ': ' Phone ', ' 2 ': ' Name ', ' 3 ': ' Sex '}test=ab//Shallow copy test2=ab.copy ()//Deep copy def out (t): for n,a in T. Items (): print '%s%s '% (n,a) printout (test) out (test2) del ab[' 1 ']out (test) out (test2)Actual use Discovery Dict.copy () function for deep copyEnvironment:

After registering deep learning on Cousera, you can download the after-class exercise.1. After entering the programming environment, click File-open in the upper left corner to enter the file management mode2. Click on the Red Circle folder to enter the root directory3. In the top right corner of the root directory, new one IPYNB4. Open the new IPYNB, enter and do the following, you can compress the files i

PrincipleShallow copyImport= Copy.copy (a)Demo>>> a=[1,['a']]>>> b=a>>> c=Copy.copy (a)>>>a[1, ['a']]>>>b[1, ['a']]>>>c[1, ['a']]>>>ID (a)140556196249680>>>ID (b)140556196249680>>>ID (c)140556298139120>>> a[0]=2>>>a[2, ['a']]>>>c[1, ['a']]>>>a[1].append ('b')>>>a[2,['a','b']]>>>c[1,['a','b']]Deep CopyImport= Copy.deepcopy (a)Demo>>> a=[1,2,['a','b']]>>> b=Copy.deepcopy (a)>>>a[1, 2, ['a','b']]>>>b[1, 2, ['a','b']]>>>ID (a)140556196175952>>>ID (b)14055

installation was successful, import the NumPy with Python, as follows to complete the installation4. Installing TensorFlow1.> download the corresponding version of the TensorFlow, must be corresponding to the Python version, the latest is the support python3.6 version, for: https://pypi.org/project/tensorflow-gpu/#files, Because my Python version is 3.6, so down