Google gave the following dataflow graph as an example without any explanation of the scenario itself (https://www.tensorflow.org/guide/graphs).
I cannot understand the use case of such a graph. Why do we need a Logit Layer on top of ReLu layer? What's the use of Softmax (cannot see any link between the output and other nodes)? What are the meanings of the four parameters (two weights and two biases)? I would like to see a real case which matches with this datagraph.
This graph is a dense layer, followed by an softmaxlayer. It is basically a neural network with one hidden layer for classification.
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I am working on a regression problem related to chess. The output will depend on about 68 values that are given by Stockfish's static evaluation function (example output shown here), as well as the state of the board. However, the static eval features should not be passed through the CNN, only through the final fully-connected layers. Therefore I want to have some convolutional layers take the (one-hot encoded) board state down to a flat vector, then extend it with the other features before passing the full vector to a fully-connected layer.
How can I use Tensorflow to combine these two feature vectors (the result from the CNN and the other game-related features) within a single Layer type that can be added to a Sequential? I couldn't find anything that would handle this in the docs. Would subclassing Layer be the only way to go?
So I am new to computer vision, and I do not really know what the layers do in keras. What is the use of adding layers (dense, Conv2D, etc) in keras? What do they add to it?
Convolution neural network has 4 main steps: Convolution, Pooling, Flatten, and Full connection.
Conv2D(), Conv3D(), etc. is for Feature extraction (It's a Convolution Layer).
Pooling layers (MaxPool2D(), AvgPool2D(), etc) is for Feature extraction as well (It has different operation though).
Flattening layers (Flatten() ) are to convert the extracted feature map into Vector before being fed into the Fully connection layers (The Dense layers).
Dense layers are for Fully connected step in Computer vision that acts as Classifier (The Neural network classify each extracted features from the Convolution layers.)
There are also optimization layers such as Dropout(), BatchNormalization(), etc.
For more information, just open the keras documentation.
If you want to start learning Convolution neural network, this article may help.
A layer in an Artificial Neural Network is a bunch of nodes bound together at a specific depth in a Neural Network. Keras is a high level API used over NN modules like TensorFlow or CNTK in order to simplify tasks. A Keras layer comprises 3 main parts:
Input Layer - Which contains the raw data
Hidden layer - Where the nodes of a layer learn some aspects about
the raw data which is input. It's similar to levels of abstraction
to form a Neural network.
Output Layer - Consists of a single output which is mostly a single
node and can be subjected to classification.
Keras, as a whole consists of many different types of layers. A Convolutional layer creates a kernel which is convoluted with the input over a single temporal space to derive a group of outputs. Pooling layers provide sampling of the feature maps by simplifying features in a map into patches. Max Pooling and Average Pooling are commonly used methods in a Pool layer.
Other commonly used layers in Keras are Embedding layers, Noise layers and Core layers. A single NN layer can represent only a Linearly seperable method. Most prediction problems are complicated and more than just one layer is required. This is where Multi Layer concept is required.
I think i clear your doubts and for any other queries you can see on https://www.tensorflow.org/api_docs/python/tf/keras
Neural networks are a great tool nowadays to automate classification problems. However when it comes to computer vision the amount of input data is too great to be handled efficiently by simple neural networks.
To reduce the network workload, your data needs to be preprocessed and certain features need to be identified. To find features in images we can use certain filters (like sobel edge detection), which will highlight the essential features needed for classification.
Again the amount of filters required to classify one image is too great, and thus the selection of those filters needs to be automated.
That's where the convolutional layer comes in.
We use a convolutional layer to generate multiple random (at first) filters that will highlight certain features in an image. While the network is training those filters are optimized to do a better job at highlighting features.
In Tensorflow we use Conv2D() to add one of those layers. An example of parameters is : Conv2D(64, 3, activation='relu'). 64 denotes the number of filters used, 3 denotes the size of the filters (in this case 3x3) and activation='relu' denotes the activation function
After the convolutional layer we use a pooling layer to further highlight the features produced by the previous convolutional layer. In Tensorflow this is usually done with MaxPooling2D() which takes the filtered image and applies a 2x2 (by default) layer every 2 pixels. The filter applied by MaxPooling is basically looking for the maximum value in that 2x2 area and adds it in a new image.
We can use this set of convolutional layer and pooling layers multiple times to make the image easier for the network to work with.
After we are done with those layers, we need to pass the output to a conventional (Dense) neural network.
To do that, we first need to flatten the image data from a 2D Tensor(Matrix) to a 1D Tensor(Vector). This is done by calling the Flatten() method.
Finally we need to add our Dense layers which are used to train on the flattened data. We do this by calling Dense(). An example of parameters is Dense(64, activation='relu')
where 64 is the number of nodes we are using.
Here is an example CNN structure I used recently:
# Build model
model = tf.keras.models.Sequential()
# Convolution and pooling layers
model.add(tf.keras.layers.Conv2D(64, 3, activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1))) # Input layer
model.add(tf.keras.layers.MaxPooling2D())
model.add(tf.keras.layers.Conv2D(64, 3, activation='relu'))
model.add(tf.keras.layers.MaxPooling2D())
# Flattened layers
model.add(tf.keras.layers.Flatten())
# Dense layers
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(2, activation='softmax')) # Output layer
Of course this worked for a certain classification problem and the number of layers and method parameters differ depending on the problem.
The Youtube channel The Coding Train has a very helpful video explaining the Convolutional and Pooling layer.
As we know, a DNN is comprised of many layers which consist of many neurons applying the same function to different parts of the input. Meanwhile, if we use Tensorflow to execute a DNN task, we will get a dataflow graph generated by Tensorflow automatically and we can use Tensorboard to visualize the dataflow graph as blow. But there is no neuron in the layer. So I wonder what is the relationship between Tensorflow dataflow graph and a DNN? When a neuron of DNN's layer map into dataflow graph, how is it represented?What is the relationship of neuron in DNN and node in tensorflow(representing an operation)? I just started to learn DNN and Tensorflow, please help me arrange thoughts in order. Thanks:) enter image description here
You have to differentiate between the metaphoric representation of a DNN and it's mathematic description. The math behind a classic neuron is the sum of the weighted inputs + a bias (usually calling a activation function on this result)
So in this case you have an input vector mutplied by a weight vector (containing trainable variables) and then summed up with a bias scalar (also trainable)
If you now consider a layer of neurons instead of one, the weights will become a matrix and the bias a vector. So calculating a feed forward layer is nothing more then a matrix multiplication follow by a sum of vectors.
This is the operation you can see in your tensorflow graph.
You can actually build your Neural Network this way without any use of the so called High Level API which use the Layer abstraction. (Many have done this in the early days of tensorflow)
The actual "magic", which tensorflow does for you is calculating and executing the derivatives of this foreword pass in order to calculate the updates for the weights.
The tensorflow config dropout wrapper has three different dropout probabilities that can be set: input_keep_prob, output_keep_prob, state_keep_prob.
I want to use variational dropout for my LSTM units, by setting the variational_recurrent argument to true. However, I don't know which of the three dropout probabilities I have to use for variational dropout to function correctly.
Can someone provide help?
According to this paper https://arxiv.org/abs/1512.05287 that is used for implementation of the variational_recurrent dropouts, you can think about as follows,
input_keep_prob - probability that dropping out input connections.
output_keep_prob - probability that dropping out output connections.
state_keep_prob - Probability that droping out recurrent connections.
See the diagram below,
If you set the variational_recurrent to be true you will get an RNN that's similar to the model in right and otherwise in left.
The basic differences in above two models are,
Variational RNN repeats the same dropout mask at each time
step for both inputs, outputs, and recurrent layers (drop
the same network units at each time step).
Native RNN uses different dropout masks at each time step for the
inputs and outputs alone (no dropout is used with the recurrent
connections since the use of different masks with these connections
leads to deteriorated performance).
In the above diagram, coloured connections represent the dropped-out connections, with different colours corresponding to different dropout masks. Dashed lines correspond to standard connections with no dropout.
Therefore, if you use a variational RNN you can set all three probability parameters according to your requirement.
Hope this helps.
It is common to add a dense fully-connected layer as the last layer on top of a recurrent neural network (which has one or more layers) in order to learn the reduction to the final output dimensionality.
Let's say I need one output with a -1 to 1 range, in which case I would use a dense layer with a tanh activation function.
My question is: Why not add another recurrent layer instead with an internal size of 1?
It will be different (in the sense of propagating that through time) but will it have a disadvantage over the dense layer?
If I understand correctly the two alternatives you present do the exact same computation, so they should behave identically.
In TensorFlow, if you're using dynamic_rnn, it's much easier if all time steps are identical, though, hence processing the output instead of having a different last step.