Category: Keras sequential layers embedding

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It only takes a minute to sign up. Need to understand the working of 'Embedding' layer in Keras library. I execute the following code in Python. But how are the mapped values computed? In fact, the output vectors are not computed from the input using any mathematical operation. Instead, each input integer is used as the index to access a table that contains all posible vectors.

keras sequential layers embedding

That is the reason why you need to specify the size of the vocabulary as the first argument so the table can be initialized. The most common application of this layer is for text processing. Let's see a simple example. Our training set consists only of two phrases:. So we can encode these phrases by assigning each word a unique integer number by order of appearance in our training dataset for example.

Then our phrases could be rewritten as:. Now imagine we want to train a network whose first layer is an embeding layer. In this case, we should initialize it as follows:. The first argument 7 is the number of distinct words in the training set. The second argument 2 indicates the size of the embedding vectors. Once the network has been trained, we can get the weights of the embedding layer, which in this case will be of size 7, 2 and can be thought as the table used to map integers to embedding vectors:.

It might seem counter intuitive at first, but the underlying automatic differentiation engines e. Hybridizing metric learning and case-based reasoning for adaptable clickbait detection.A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.

Note that there's also a corresponding pop method to remove layers: a Sequential model behaves very much like a list of layers. Also note that the Sequential constructor accepts a name argument, just like any layer or model in Keras.

This is useful to annotate TensorBoard graphs with semantically meaningful names. Generally, all layers in Keras need to know the shape of their inputs in order to be able to create their weights. So when you create a layer like this, initially, it has no weights:. It creates its weights the first time it is called on an input, since the shape of the weights depends on the shape of the inputs:.

Naturally, this also applies to Sequential models. When you instantiate a Sequential model without an input shape, it isn't "built": it has no weights and calling model. The weights are created when the model first sees some input data:. However, it can be very useful when building a Sequential model incrementally to be able to display the summary of the model so far, including the current output shape. In this case, you should start your model by passing an Input object to your model, so that it knows its input shape from the start:.

Note that the Input object is not displayed as part of model.

Models built with a predefined input shape like this always have weights even before seeing any data and always have a defined output shape. In general, it's a recommended best practice to always specify the input shape of a Sequential model in advance if you know what it is. When building a new Sequential architecture, it's useful to incrementally stack layers with add and frequently print model summaries.

For instance, this enables you to monitor how a stack of Conv2D and MaxPooling2D layers is downsampling image feature maps:. This means that every layer has an input and output attribute. These attributes can be used to do neat things, like quickly creating a model that extracts the outputs of all intermediate layers in a Sequential model:. Transfer learning consists of freezing the bottom layers in a model and only training the top layers.

If you aren't familiar with it, make sure to read our guide to transfer learning.

Masking and padding with Keras

First, let's say that you have a Sequential model, and you want to freeze all layers except the last one. In this case, you would simply iterate over model. Like this:. Another common blueprint is to use a Sequential model to stack a pre-trained model and some freshly initialized classification layers.

If you do transfer learning, you will probably find yourself frequently using these two patterns. Sequential [ layers. Dense 4]. Sequential model.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am using Keras tensorflow backend and am wondering how to add multiple Embedding layers into a Keras Sequential model.

More specifically, I have several columns in my dataset which have categorical values and I have considered using one-hot encoding but have determined that the number of categorical items is in the hundreds leading to a large and far too sparse set of columns.

Upon looking for solutions I have found that Keras' Embedding layer appears to solve the problem very elegantly. However, most of the examples and Keras documentation illustrate a very simple situation with one Embedding layer.

Unfortunately, I do not know how to integrate the multiple Embedding layers as input into a single model. My code looks like this, but it does not work, and I am guessing that the multiple Embedding layers act sequentially first Embedding layer is input the the second and so on rather than be a multiple input sources to the model:.

My question is how would I establish a Keras Sequential model such that I would be able to use the three Embedding layers shown above. What specifically goes in between the first and last layers:. Am I on the right track, or is my approach incorrect and I need to establish the model in a different manner? This can be done easily if you switch to the functional APIfirst have a read. Then you can build a model with multiple inputs that represent different columns:. The gist of this the layers are callable objects that build the computation graph.

You can also for example share embedding layers between columns by simply using the same Embedding layer. Learn more. Asked 2 years, 4 months ago. Active 2 years, 1 month ago. Viewed 1k times. Eric Broda Eric Broda 4, 6 6 gold badges 27 27 silver badges 49 49 bronze badges. Active Oldest Votes.Masking is a way to tell sequence-processing layers that certain timesteps in an input are missing, and thus should be skipped when processing the data.

Padding is a special form of masking where the masked steps are at the start or at the beginning of a sequence. Padding comes from the need to encode sequence data into contiguous batches: in order to make all sequences in a batch fit a given standard length, it is necessary to pad or truncate some sequences. When processing sequence data, it is very common for individual samples to have different lengths. Consider the following example text tokenized as words :.

The data is a nested list where individual samples have length 3, 5, and 6, respectively. Since the input data for a deep learning model must be a single tensor of shape e. Keras provides a utility function to truncate and pad Python lists to a common length: tf.

Now that all samples have a uniform length, the model must be informed that some part of the data is actually padding and should be ignored. That mechanism is masking. Keras will automatically fetch the mask corresponding to an input and pass it to any layer that knows how to use it. For instance, in the following Sequential model, the LSTM layer will automatically receive a mask, which means it will ignore padded values:. Meanwhile, layers that produce a mask e. Sometimes, you may need to write layers that generate a mask like Embeddingor layers that need to modify the current mask.

For instance, any layer that produces a tensor with a different time dimension than its input, such as a Concatenate layer that concatenates on the time dimension, will need to modify the current mask so that downstream layers will be able to properly take masked timesteps into account. To do this, your layer should implement the layer. Here is another example of a CustomEmbedding layer that is capable of generating a mask from input values:.

Most layers don't modify the time dimension, so don't need to modify the current mask. However, they may still want to be able to propagate the current mask, unchanged, to the next layer.

This is an opt-in behavior. By default, a custom layer will destroy the current mask since the framework has no way to tell whether propagating the mask is safe to do. If you have a custom layer that does not modify the time dimension, and if you want it to be able to propagate the current input mask, you should set self. You can now use this custom layer in-between a mask-generating layer like Embedding and a mask-consuming layer like LSTMand it will pass the mask along so that it reaches the mask-consuming layer.

Some layers are mask consumers : they accept a mask argument in call and use it to determine whether to skip certain time steps. The mask associated with the inputs will be passed to your layer whenever it is available. Here's a simple example below: a layer that computes a softmax over the time dimension axis 1 of an input sequence, while discarding masked timesteps.

Note that you could "pre" padding at the beginning or "post" padding at the end. Masking Simulate the embedding lookup by expanding the 2D input to 3D, with embedding dimension of Sequential [ layers.

Word2Vec - Skipgram and CBOW

LSTM 32 ,]. Model inputsoutputs.Masking is a way to tell sequence-processing layers that certain timesteps in an input are missing, and thus should be skipped when processing the data. Padding is a special form of masking where the masked steps are at the start or at the beginning of a sequence.

Embedding layer

Padding comes from the need to encode sequence data into contiguous batches: in order to make all sequences in a batch fit a given standard length, it is necessary to pad or truncate some sequences. When processing sequence data, it is very common for individual samples to have different lengths. Consider the following example text tokenized as words :.

The data is a nested list where individual samples have length 3, 5, and 6, respectively.

keras sequential layers embedding

Since the input data for a deep learning model must be a single tensor of shape e. Keras provides a utility function to truncate and pad Python lists to a common length: tf. Now that all samples have a uniform length, the model must be informed that some part of the data is actually padding and should be ignored. That mechanism is masking.

Keras will automatically fetch the mask corresponding to an input and pass it to any layer that knows how to use it. For instance, in the following Sequential model, the LSTM layer will automatically receive a mask, which means it will ignore padded values:.

Meanwhile, layers that produce a mask e. Sometimes, you may need to write layers that generate a mask like Embeddingor layers that need to modify the current mask. For instance, any layer that produces a tensor with a different time dimension than its input, such as a Concatenate layer that concatenates on the time dimension, will need to modify the current mask so that downstream layers will be able to properly take masked timesteps into account.

To do this, your layer should implement the layer. Here is an example of a TemporalSplit layer that needs to modify the current mask. Here is another example of a CustomEmbedding layer that is capable of generating a mask from input values:.

Most layers don't modify the time dimension, so don't need to modify the current mask. However, they may still want to be able to propagate the current mask, unchanged, to the next layer.

Understanding masking & padding

This is an opt-in behavior. By default, a custom layer will destroy the current mask since the framework has no way to tell whether propagating the mask is safe to do.

If you have a custom layer that does not modify the time dimension, and if you want it to be able to propagate the current input mask, you should set self.As we debated this tendency while making our predictions, we wondered if, in the clash between pragmatism and individualism, brand loyalty would stay low, increase, or even decline.

Ultimately, we decided it would increase as the emotional benefits of brands became more important to consumers and as increased choice and availability of branded products (online and off) would allow consumers to optimize for price and convenience without changing choices too often.

Our recent research confirmed the changes we anticipated. Consumers are now significantly less likely to buy a brand that is not already among their favorites, continuing the upward trend we observed in 2011 (Exhibit 4).

The modern shopper Our 2011 predictions were bullish on e-commerce, predicting that Chinese consumers would adapt their channel choices even faster than has occurred in developed markets. We estimated that by 2020, online consumer-electronics purchases would jump to 40 percent, from about 10 percent. More mainstream categories would rise to 15 percent, and some categories, such as groceries (now below 1 percent), could reach about 10 percent.

These changes are occurring even as the enduring pragmatism and diligence of the Chinese consumer continue to be in place. Our latest research shows that consumers of all age groups are much more likely to collect information online, even on fast-moving consumer goods, than they were just three years ago.

In 2015, online food and beverages sales (excluding fresh) reached 7. The online share of consumer-electronic purchases, meanwhile, has reached a whopping 39 percent in 2015, and it now looks possible that by 2020 it will be about 50 percent of overall sales.

Making predictions may be difficult, especially about the futureas US Baseball Hall of Famer Yogi Berra famously observed. But they can still provide valuable foresight for executives. Create a profile to get full access to our articles and reports, including those by McKinsey Quarterly and the McKinsey Global Institute, and to subscribe to our newsletters and email alerts.

McKinsey Quarterly Our flagship business publication has been defining and informing the senior-management agenda since 1964. McKinsey Academy Our learning programs help organizations accelerate growth by unlocking their people's potential. What the future of work will mean for jobs, skills, and wages Report - McKinsey Global Institute 2. Five Fifty: Becoming CEO Interactive - McKinsey Quarterly 3. Ten trends redefining enterprise IT infrastructure Article 4.

In search of a better stretch target Article 5.If your smartphone or Internet use is affecting your partner directly, as with excessive use of Internet pornography or online affairs, marriage counseling can help you work through these challenging issues.

Marriage counseling can also help you reconnect with your partner if you have been using virtual worlds for most of your social needs. Of course, online support groups and forums should be used with caution. Sex Addicts Anonymous may be another place to try if you are having trouble with cybersex or compulsive use of sex and dating apps.

For those in need of greater intervention, there are now specialist treatment centers that offer digital detox programs to help you disconnect from digital media. For help finding these, as well as support groups and therapists, see the Resources and References section below.

The American Academy of Pediatrics (AAP) recommends that kids under age 2 have no screen time, while young children older than 2 should spend no more than 1 to 2 hours a day viewing age-appropriate material. Of course, once kids have their own smartphones, limiting their use becomes that much more difficult. Youngsters lack the maturity to curb their smartphone use on their own, but simply confiscating the device can often backfire, creating anxiety and withdrawal symptoms in your child.

Instead, there are plenty of other ways to help your child find a healthier balance: Be a good role model. Try not to let your own smartphone use distract from parent-child interactions.

Other apps can eliminate messaging capabilities while in motion, so you can prevent your teen using a smartphone while driving. Restrict the use of smartphones or tablets to a common area of the house where you can keep an eye on your child's activity and limit time online. Encourage other interests and social activities. Get your child out from behind the phone or computer screen. Expose kids to other hobbies and activities, such as team sports, Scouts, and afterschool clubs.

Spend time as a family unplugged. Talk to your child about underlying issues.

keras sequential layers embedding

Compulsive smartphone use can be the sign of deeper problems. Is your child having problems fitting in. Has there been a recent major change, like a move or divorce, which is causing stress. Is your child suffering with other issues at school or home.

Teenagers often rebel against their parents, but if they hear the same information from a different authority figure, they may be more inclined to listen. Try a sports coach, doctor, or respected family friend. Jennifer Schneider discusses the types and consequences of cybersex addiction.

Has locations in New York, California, Texas, and North Carolina. Last updated: October 2017. This site is for information only and NOT a substitute for professional diagnosis and treatment. The content of this reprint is for informational purposes only and NOT a substitute for professional advice, diagnosis, or treatment. Effects of smartphone addiction Signs and symptoms of smartphone addiction Are you addicted to your smartphone.

Self-help tips for smartphone addiction Treatment for smartphone addiction Helping a child or teen with smartphone addiction Related articles Topic PageAddictions Smartphone Addiction Tips for Breaking Free of Compulsive Smartphone Use While a smartphone, tablet, or computer can be a hugely productive tool, compulsive use of these devices can interfere with your daily life, work, and relationships.

What is smartphone addiction. Smartphone addiction can encompass a variety of impulse-control problems, including: Virtual relationships. Cybersex addiction Compulsive use of Internet pornography, sexting, nude-swapping, adult chat rooms, or messaging services can impact negatively on your real-life intimate relationships and overall emotional health. Withdrawal symptoms from smartphone addiction A common warning sign of smartphone or Internet addiction is experiencing withdrawal symptoms when you try to cut back on your smartphone use.

These may include: Restlessness Anger or irritability Difficulty concentrating Sleep problems Craving access to your smartphone or other device Are you addicted to your smartphone.


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