In his 1982 paper, Hopfield wanted to address the fundamental question of emergence in cognitive systems: Can relatively stable cognitive phenomena, like memories, emerge from the collective action of large numbers of simple neurons? Yet, there are some implementation issues with the optimizer that require importing from Tensorflow to work. Launching the CI/CD and R Collectives and community editing features for Can Keras with Tensorflow backend be forced to use CPU or GPU at will? Hopfield networks[1][4] are recurrent neural networks with dynamical trajectories converging to fixed point attractor states and described by an energy function. {\displaystyle C\cong {\frac {n}{2\log _{2}n}}} A simple example[7] of the modern Hopfield network can be written in terms of binary variables ( , 80.3 second run - successful. g The second role is the core idea behind LSTM. n The explicit approach represents time spacially. [4] A major advance in memory storage capacity was developed by Krotov and Hopfield in 2016[7] through a change in network dynamics and energy function. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, The open-source game engine youve been waiting for: Godot (Ep. Nevertheless, LSTM can be trained with pure backpropagation. are denoted by Once a corpus of text has been parsed into tokens, we have to map such tokens into numerical vectors. {\displaystyle V^{s}}, w {\displaystyle g_{i}} As in previous blogpost, Ill use Keras to implement both (a modified version of) the Elman Network for the XOR problem and an LSTM for review prediction based on text-sequences. x {\displaystyle s_{i}\leftarrow \left\{{\begin{array}{ll}+1&{\text{if }}\sum _{j}{w_{ij}s_{j}}\geq \theta _{i},\\-1&{\text{otherwise.}}\end{array}}\right.}. f Here Ill briefly review these issues to provide enough context for our example applications. In this sense, the Hopfield network can be formally described as a complete undirected graph x 542), How Intuit democratizes AI development across teams through reusability, We've added a "Necessary cookies only" option to the cookie consent popup. {\displaystyle f_{\mu }} $W_{hz}$ at time $t$, the weight matrix for the linear function at the output layer. {\textstyle g_{i}=g(\{x_{i}\})} The rest remains the same. For instance, my Intel i7-8550U took ~10 min to run five epochs. . We have two cases: Now, lets compute a single forward-propagation pass: We see that for $W_l$ the output $\hat{y}\approx4$, whereas for $W_s$ the output $\hat{y} \approx 0$. i ( {\displaystyle V} i In this case the steady state solution of the second equation in the system (1) can be used to express the currents of the hidden units through the outputs of the feature neurons. Found 1 person named Brooke Woosley along with free Facebook, Instagram, Twitter, and TikTok search on PeekYou - true people search. We do this because training RNNs is computationally expensive, and we dont have access to enough hardware resources to train a large model here. Hopfield -11V Hopfield1ijW 14Hopfield VW W i This allows the net to serve as a content addressable memory system, that is to say, the network will converge to a "remembered" state if it is given only part of the state. {\displaystyle w_{ii}=0} to the feature neuron Use Git or checkout with SVN using the web URL. Hopfield would use a nonlinear activation function, instead of using a linear function. In the same paper, Elman showed that the internal (hidden) representations learned by the network grouped into meaningful categories, this is, semantically similar words group together when analyzed with hierarchical clustering. (see the Updates section below). being a continuous variable representingthe output of neuron x , What's the difference between a Tensorflow Keras Model and Estimator? It has But, exploitation in the context of labor rights is related to the idea of abuse, hence a negative connotation. m {\displaystyle L(\{x_{I}\})} {\displaystyle V_{i}} 1 [1] Networks with continuous dynamics were developed by Hopfield in his 1984 paper. and the activation functions (2017). These Hopfield layers enable new ways of deep learning, beyond fully-connected, convolutional, or recurrent networks, and provide pooling, memory, association, and attention mechanisms. One can even omit the input x and merge it with the bias b: the dynamics will only depend on the initial state y 0. y t = f ( W y t 1 + b) Fig. 10. Second, it imposes a rigid limit on the duration of pattern, in other words, the network needs a fixed number of elements for every input vector $\bf{x}$: a network with five input units, cant accommodate a sequence of length six. $h_1$ depens on $h_0$, where $h_0$ is a random starting state. It is almost like the system remembers its previous stable-state (isnt?). Connect and share knowledge within a single location that is structured and easy to search. Note that this energy function belongs to a general class of models in physics under the name of Ising models; these in turn are a special case of Markov networks, since the associated probability measure, the Gibbs measure, has the Markov property. As with any neural network, RNN cant take raw text as an input, we need to parse text sequences and then map them into vectors of numbers. These two elements are integrated as a circuit of logic gates controlling the flow of information at each time-step. {\displaystyle V_{i}=+1} There is no learning in the memory unit, which means the weights are fixed to $1$. } ( For example, since the human brain is always learning new concepts, one can reason that human learning is incremental. Biological neural networks have a large degree of heterogeneity in terms of different cell types. Consider the task of predicting a vector $y = \begin{bmatrix} 1 & 1 \end{bmatrix}$, from inputs $x = \begin{bmatrix} 1 & 1 \end{bmatrix}$, with a multilayer-perceptron with 5 hidden layers and tanh activation functions. Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996). Consider the connection weight For instance, even state-of-the-art models like OpenAI GPT-2 sometimes produce incoherent sentences. {\displaystyle x_{I}} {\displaystyle w_{ij}} = {\displaystyle w_{ij}} i {\displaystyle \tau _{I}} The math reviewed here generalizes with minimal changes to more complex architectures as LSTMs. The amount that the weights are updated during training is referred to as the step size or the " learning rate .". i Furthermore, both types of operations are possible to store within a single memory matrix, but only if that given representation matrix is not one or the other of the operations, but rather the combination (auto-associative and hetero-associative) of the two. {\displaystyle U_{i}} Many to one and many to many LSTM examples in Keras, Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2. Demo train.py The following is the result of using Synchronous update. Rather, during any kind of constant initialization, the same issue happens to occur. By adding contextual drift they were able to show the rapid forgetting that occurs in a Hopfield model during a cued-recall task. Manning. In associative memory for the Hopfield network, there are two types of operations: auto-association and hetero-association. i Note that, in contrast to Perceptron training, the thresholds of the neurons are never updated. There's also live online events, interactive content, certification prep materials, and more. For instance, for the set $x= {cat, dog, ferret}$, we could use a 3-dimensional one-hot encoding as: One-hot encodings have the advantages of being straightforward to implement and to provide a unique identifier for each token. This unrolled RNN will have as many layers as elements in the sequence. i The network is assumed to be fully connected, so that every neuron is connected to every other neuron using a symmetric matrix of weights Our client is currently seeking an experienced Sr. AI Sensor Fusion Algorithm Developer supporting our team in developing the AI sensor fusion software architectures for our next generation radar products. Notice that every pair of units i and j in a Hopfield network has a connection that is described by the connectivity weight Jarne, C., & Laje, R. (2019). For a detailed derivation of BPTT for the LSTM see Graves (2012) and Chen (2016). ) (as in the binary model), and a second term which depends on the gain function (neuron's activation function). 1 In a strict sense, LSTM is a type of layer instead of a type of network. Figure 6: LSTM as a sequence of decisions. is a set of McCullochPitts neurons and A Hopfield network is a form of recurrent ANN. Get full access to Keras 2.x Projects and 60K+ other titles, with free 10-day trial of O'Reilly. {\displaystyle i} x 0 i enumerates the layers of the network, and index Now, imagine $C_1$ yields a global energy-value $E_1= 2$ (following the energy function formula). Nevertheless, problems like vanishing gradients, exploding gradients, and computational inefficiency (i.e., lack of parallelization) have difficulted RNN use in many domains. Psychology Press. We can simply generate a single pair of training and testing sets for the XOR problem as in Table 1, and pass the training sequence (length two) as the inputs, and the expected outputs as the target. Finally, we wont worry about training and testing sets for this example, which is way to simple for that (we will do that for the next example). Yet, Ill argue two things. [4] Hopfield networks also provide a model for understanding human memory.[5][6]. The connections in a Hopfield net typically have the following restrictions: The constraint that weights are symmetric guarantees that the energy function decreases monotonically while following the activation rules. j Nevertheless, learning embeddings for every task sometimes is impractical, either because your corpus is too small (i.e., not enough data to extract semantic relationships), or too large (i.e., you dont have enough time and/or resources to learn the embeddings). V Bhiksha Rajs Deep Learning Lectures 13, 14, and 15 at CMU. 3 For each stored pattern x, the negation -x is also a spurious pattern. was defined,and the dynamics consisted of changing the activity of each single neuron 2 i . Still, RNN has many desirable traits as a model of neuro-cognitive activity, and have been prolifically used to model several aspects of human cognition and behavior: child behavior in an object permanence tasks (Munakata et al, 1997); knowledge-intensive text-comprehension (St. John, 1992); processing in quasi-regular domains, like English word reading (Plaut et al., 1996); human performance in processing recursive language structures (Christiansen & Chater, 1999); human sequential action (Botvinick & Plaut, 2004); movement patterns in typical and atypical developing children (Muoz-Organero et al., 2019). Thus, a sequence of 50 words will be unrolled as an RNN of 50 layers (taking word as a unit). Next, we need to pad each sequence with zeros such that all sequences are of the same length. h In short, the memory unit keeps a running average of all past outputs: this is how the past history is implicitly accounted for on each new computation. Therefore, the Hopfield network model is shown to confuse one stored item with that of another upon retrieval. ( The results of these differentiations for both expressions are equal to ( Started in any initial state, the state of the system evolves to a final state that is a (local) minimum of the Lyapunov function . f If you run this, it may take around 5-15 minutes in a CPU. where {\displaystyle V^{s'}} Therefore, it is evident that many mistakes will occur if one tries to store a large number of vectors. h As with Convolutional Neural Networks, researchers utilizing RNN for approaching sequential problems like natural language processing (NLP) or time-series prediction, do not necessarily care about (although some might) how good of a model of cognition and brain-activity are RNNs. Comments (0) Run. { Please i V s i n h w It is generally used in performing auto association and optimization tasks. You can think about elements of $\bf{x}$ as sequences of words or actions, one after the other, for instance: $x^1=[Sound, of, the, funky, drummer]$ is a sequence of length five. Ethan Crouse 30 Followers . ( The Hebbian Theory was introduced by Donald Hebb in 1949, in order to explain "associative learning", in which simultaneous activation of neuron cells leads to pronounced increases in synaptic strength between those cells. i Is lack of coherence enough? j {\displaystyle B} {\displaystyle x_{i}^{A}} i CAM works the other way around: you give information about the content you are searching for, and the computer should retrieve the memory. if g B The most likely explanation for this was that Elmans starting point was Jordans network, which had a separated memory unit. i Just think in how many times you have searched for lyrics with partial information, like song with the beeeee bop ba bodda bope!. Raj, B. 502Port Orvilleville, ON H8J-6M9 (719) 696-2375 x665 [email protected] Chart 2 shows the error curve (red, right axis), and the accuracy curve (blue, left axis) for each epoch. i This kind of initialization is highly ineffective as neurons learn the same feature during each iteration. What's the difference between a power rail and a signal line? {\textstyle i} . h https://doi.org/10.1016/j.conb.2017.06.003. A V Sequence Modeling: Recurrent and Recursive Nets. sgn GitHub is where people build software. Christiansen, M. H., & Chater, N. (1999). 1 {\displaystyle g_{J}} [8] The continuous dynamics of large memory capacity models was developed in a series of papers between 2016 and 2020. Supervised sequence labelling. is the threshold value of the i'th neuron (often taken to be 0). Training a Hopfield net involves lowering the energy of states that the net should "remember". Doing without schema hierarchies: A recurrent connectionist approach to normal and impaired routine sequential action. If you look at the diagram in Figure 6, $f_t$ performs an elementwise multiplication of each element in $c_{t-1}$, meaning that every value would be reduced to $0$. ) This is more critical when we are dealing with different languages. Lightish-pink circles represent element-wise operations, and darkish-pink boxes are fully-connected layers with trainable weights. is a zero-centered sigmoid function. Following the rules of calculus in multiple variables, we compute them independently and add them up together as: Again, we have that we cant compute $\frac{\partial{h_2}}{\partial{W_{hh}}}$ directly. You can think about it as making three decisions at each time-step: Decisions 1 and 2 will determine the information that keeps flowing through the memory storage at the top. The Ising model of a neural network as a memory model was first proposed by William A. {\displaystyle J_{pseudo-cut}(k)=\sum _{i\in C_{1}(k)}\sum _{j\in C_{2}(k)}w_{ij}+\sum _{j\in C_{1}(k)}{\theta _{j}}}, where N [4] The energy in the continuous case has one term which is quadratic in the Does With(NoLock) help with query performance? 1 2 Why is there a memory leak in this C++ program and how to solve it, given the constraints? w All the above make LSTMs sere](https://en.wikipedia.org/wiki/Long_short-term_memory#Applications)). {\displaystyle G=\langle V,f\rangle } . Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, \Lukasz, & Polosukhin, I. J The mathematics of gradient vanishing and explosion gets complicated quickly. j Hence, we have to pad every sequence to have length 5,000. True, you could start with a six input network, but then shorter sequences would be misrepresented since mismatched units would receive zero input. Deep learning: A critical appraisal. Neural Networks, 3(1):23-43, 1990. You signed in with another tab or window. Table 1 shows the XOR problem: Here is a way to transform the XOR problem into a sequence. This learning rule is local, since the synapses take into account only neurons at their sides. Cognitive Science, 16(2), 271306. This was remarkable as demonstrated the utility of RNNs as a model of cognition in sequence-based problems. N . Continuous Hopfield Networks for neurons with graded response are typically described[4] by the dynamical equations, where Why was the nose gear of Concorde located so far aft? ) Ill train the model for 15,000 epochs over the 4 samples dataset. As traffic keeps increasing, en route capacity, especially in Europe, becomes a serious problem. Now, keep in mind that this sequence of decision is just a convenient interpretation of LSTM mechanics. I Critics like Gary Marcus have pointed out the apparent inability of neural-networks based models to really understand their outputs (Marcus, 2018). Indeed, in all models we have examined so far we have implicitly assumed that data is perceived all at once, although there are countless examples where time is a critical consideration: movement, speech production, planning, decision-making, etc. 0 arrow_right_alt. . j Consequently, when doing the weight update based on such gradients, the weights closer to the output layer will obtain larger updates than weights closer to the input layer. In LSTMs, instead of having a simple memory unit cloning values from the hidden unit as in Elman networks, we have a (1) cell unit (a.k.a., memory unit) which effectively acts as long-term memory storage, and (2) a hidden-state which acts as a memory controller. {\displaystyle A} Neural Networks: Hopfield Nets and Auto Associators [Lecture]. , Originally, Hochreiter and Schmidhuber (1997) trained LSTMs with a combination of approximate gradient descent computed with a combination of real-time recurrent learning and backpropagation through time (BPTT). Indeed, memory is what allows us to incorporate our past thoughts and behaviors into our future thoughts and behaviors. is introduced to the neural network, the net acts on neurons such that. I If you keep cycling through forward and backward passes these problems will become worse, leading to gradient explosion and vanishing respectively. Artificial Neural Networks (ANN) - Keras. Precipitation was either considered an input variable on its own or . A tag already exists with the provided branch name. The idea of using the Hopfield network in optimization problems is straightforward: If a constrained/unconstrained cost function can be written in the form of the Hopfield energy function E, then there exists a Hopfield network whose equilibrium points represent solutions to the constrained/unconstrained optimization problem. This significantly increments the representational capacity of vectors, reducing the required dimensionality for a given corpus of text compared to one-hot encodings. Graves, A. {\displaystyle V_{i}} To put it plainly, they have memory. http://deeplearning.cs.cmu.edu/document/slides/lec17.hopfield.pdf. Two common ways to do this are one-hot encoding approach and the word embeddings approach, as depicted in the bottom pane of Figure 8. Zero Initialization. Second, Why should we expect that a network trained for a narrow task like language production should understand what language really is? If you are curious about the review contents, the code snippet below decodes the first review into words. i layer The neurons can be organized in layers so that every neuron in a given layer has the same activation function and the same dynamic time scale. We can download the dataset by running the following: Note: This time I also imported Tensorflow, and from there Keras layers and models. The activation function for each neuron is defined as a partial derivative of the Lagrangian with respect to that neuron's activity, From the biological perspective one can think about Defining a (modified) in Keras is extremely simple as shown below. There are no synaptic connections among the feature neurons or the memory neurons. 79 no. i j w A fascinating aspect of Hopfield networks, besides the introduction of recurrence, is that is closely based in neuroscience research about learning and memory, particularly Hebbian learning (Hebb, 1949). p [18] It is often summarized as "Neurons that fire together, wire together. We begin by defining a simplified RNN as: Where $h_t$ and $z_t$ indicates a hidden-state (or layer) and the output respectively. The Hopfield model accounts for associative memory through the incorporation of memory vectors. k For a given corpus of text has been parsed into tokens, have! Like language production should understand what language really is figure 6: LSTM as unit! [ 6 ] second, Why should we expect that a network trained for a narrow task language... Linear function recurrent connectionist approach to normal and impaired routine sequential action model... Learning new concepts, one can reason that human learning is incremental memory unit of,... Never updated through the incorporation of memory vectors a continuous variable representingthe output neuron..., especially in Europe, becomes a serious problem a model of cognition in problems. Network, which had a separated memory unit remains the same term which depends on the gain function neuron! In associative memory through the incorporation of memory vectors variable on its own or problem into a sequence of is. Acts on neurons such that all sequences are of the same feature during each iteration around 5-15 minutes in CPU! V sequence Modeling: recurrent and Recursive Nets had a separated memory unit that fire together, wire together almost... With the provided branch name sequence of decision is just a convenient interpretation of LSTM mechanics required. Can reason that human learning is incremental single neuron 2 i biological neural networks Hopfield! The threshold value of the i'th neuron ( often taken to be 0 ). and. Of different cell types en route capacity, especially in Europe, becomes a serious problem x_ i... Get full access to Keras 2.x Projects and 60K+ other titles, with free 10-day trial of O'Reilly of:... } ) } the rest remains the same feature during each iteration, reducing the required dimensionality for narrow... 5 ] [ 6 ], with free 10-day trial of O'Reilly to map such tokens into numerical.! Layers with trainable weights network, which had a separated memory unit cell types a circuit of gates... The gain function ( neuron 's activation function ). parsed into tokens we. Or checkout with SVN using the web URL, and 15 at CMU )! Has But, exploitation in the context of labor rights is related to the neuron. As many layers as elements in the context of labor rights is related to the neural network as sequence! } to the neural network as a unit ). training, the code snippet decodes... Neuron x, what 's the difference between a power rail and a Hopfield model accounts for memory! This unrolled RNN will have as many layers as elements in the context of labor rights is to... With free 10-day trial of O'Reilly, K. ( 1996 ). for... Signal line 10-day trial of O'Reilly leading to gradient explosion and vanishing respectively of RNNs as a circuit of gates. $ is a type of layer instead of a type of network to the idea of abuse, a! Rapid forgetting that occurs in a strict sense, LSTM can be trained with pure backpropagation memory through the of! Negative connotation doing without schema hierarchies: a recurrent connectionist approach to normal and routine! Is a type of layer instead of using a linear function contrast to Perceptron training, the same happens... Hopfield network, the same feature during each iteration initialization, the Hopfield network is set. Models like OpenAI GPT-2 sometimes produce incoherent sentences connect and share knowledge within single! Remembers its previous stable-state ( isnt? ). used in performing auto association and optimization tasks example applications most.: Hopfield Nets and auto Associators [ Lecture ] at CMU lightish-pink circles represent element-wise operations, and darkish-pink are. Sequences are of the neurons are never updated separated memory unit ) and Chen ( )! The i'th neuron ( often taken to be 0 ). around 5-15 minutes in a Hopfield model a... Neurons are never updated as elements in the sequence \displaystyle w_ { ii } =0 to. Local, since the human brain is always learning new concepts, one can reason that learning... A recurrent connectionist approach to normal and impaired routine sequential action together, wire together by adding drift... Hopfield networks also provide a model of cognition in sequence-based problems of:... That human learning is incremental have to pad every sequence to have 5,000. Share knowledge within a single location that is structured and easy to search 1999... Explosion and vanishing respectively changing the activity of each single neuron 2 i ( 1996 ) ). 60K+ other titles, with free 10-day trial of O'Reilly binary model ), and at... A way to transform the XOR problem: Here is a form of recurrent ANN degree. Therefore, the thresholds of the i'th neuron ( often taken to 0! Neural networks: Hopfield Nets and auto Associators [ Lecture ] rapid forgetting that occurs a... Mcclelland, J. L., Seidenberg, M. S., & Patterson, K. ( 1996 ) )! Hopfield Nets and auto Associators [ Lecture ] stored pattern x, the code snippet below decodes the review! 3 for hopfield network keras stored pattern x, the Hopfield network is a type network... Have a large degree of heterogeneity in terms of different cell types and Associators... Cycling through forward and backward passes these problems will become worse, leading to gradient explosion and vanishing respectively contrast. } =g ( \ { x_ { i } \ hopfield network keras ) } the rest the! ) } the rest remains the same feature during each iteration i7-8550U took ~10 min to run five.... In performing auto association and optimization tasks synaptic connections among the feature neurons or the neurons. For associative memory for the Hopfield network is a random starting state 1 2 Why there. Neurons or the memory neurons the optimizer that require importing from Tensorflow to.... Auto association and optimization tasks demonstrated the utility of RNNs as a model for understanding human memory. [ ]... A circuit of logic gates controlling the flow of information at each time-step { x_ { i } \ )... Hopfield Nets and auto Associators [ Lecture ] \displaystyle a } neural networks have a large degree of in. This significantly increments the representational capacity of vectors, reducing the required dimensionality for a narrow task language! This C++ program and how to solve it, given the constraints for,... Even state-of-the-art models like OpenAI GPT-2 sometimes produce incoherent sentences auto Associators [ Lecture ] drift! To pad each sequence with zeros such that in contrast to Perceptron training, the code snippet decodes... 6 ] free 10-day trial of O'Reilly is often summarized as `` neurons that together. Expect that a network trained for a detailed derivation of BPTT for the Hopfield model... New concepts, one can reason that human learning is incremental samples dataset a linear function easy. Integrated as a sequence of decision is just a convenient interpretation of LSTM mechanics produce incoherent.... Are integrated as a circuit of logic gates controlling the flow of information at each time-step operations and! Human brain is always learning new concepts, one can reason that human learning incremental! Put it plainly, they have memory. [ 5 ] [ 6 ] a Tensorflow Keras and. A spurious pattern with trainable weights see Graves ( 2012 ) and Chen ( 2016 ) )! Git or checkout with SVN using the web URL taken to be hopfield network keras.! W it is generally used in performing auto association and optimization tasks neural network as a sequence of decisions a. Drift they were able to show the rapid forgetting that occurs in a CPU is to., 1990 is related to the idea of abuse, hence a negative connotation the value... The flow of information at each time-step types of operations: auto-association and hetero-association this unrolled RNN have! William a able to show the rapid forgetting that occurs in a Hopfield network is type! And 15 at CMU LSTM can be trained with pure backpropagation share knowledge within a single location is! A Hopfield net involves lowering the energy of states that the net acts neurons! Code snippet below decodes the first review into words explanation for this was remarkable hopfield network keras. The flow of information at each time-step on neurons such that (?! Science, 16 ( 2 ), 271306 table 1 shows the XOR problem: Here is a of. Between a Tensorflow Keras model and Estimator layer instead of using Synchronous update is introduced to the feature neuron Git... Indeed, memory is what allows us to incorporate our past thoughts and behaviors for,. Remember '' a separated memory unit Once a corpus of text compared to one-hot encodings LSTM! A neural network as a circuit of logic gates controlling the flow of at! During each iteration a large degree of heterogeneity in terms of different cell types the! The XOR problem into a sequence of 50 words will be unrolled as RNN... Perceptron training, the Hopfield model accounts for associative memory for the LSTM see Graves ( ). 1 in a CPU the review contents, the net acts on neurons such that all sequences of. Corpus of text compared to one-hot encodings feature neurons or the memory neurons and 15 at.! Remains the same issue happens to occur McCullochPitts neurons and a signal line, LSTM is type... Net should `` remember '':23-43, 1990 shows the XOR problem into a sequence of decision just... A cued-recall task } the rest remains the same issue happens to occur for example, since human. Will be unrolled as an RNN of 50 layers ( taking word a. Defined, and more either considered an input variable on its own or schema hierarchies: recurrent! Kind of initialization is highly ineffective as neurons learn the same issue happens to occur 1 Why...