Introduction to Keras#
Import Keras#
import keras
Layers in Deep Learning#
Input layer
Dense (fully connected) layers
Recurrent layer (use for model with time series data)
Convolution layer (use for model with image data)
Other layers
For regular Deep Learning model, we use fully connected or Dense layer:
from tensorflow.keras.models import Sequential
from tensroflow.keras.layers import Dense
Sequential#
A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.
More information can be found here
Dense:#
Dense implements the operation: output = activation(dot(input, kernel) + bias); where:
activation is the element-wise activation function passed as the activation argument,
kernel is a weights matrix created by the layer,
bias is a bias vector created by the layer (only applicable if use_bias is True).
More information on Dense can be found here
Create a Sequential model with N=2 as in image above:#
# Create a Sequential model
model = Sequential()
# Create a first hidden layer, the input for the first hidden layer is input layer which has 3 variables:
model.add(Dense(5, activation='relu', input_shape=(3,)))
# Create a second hidden layer without specifying input_shape
model.add(Dense(4, activation='relu'))
# Create an output layer:
model.add(Dense(2,activation='sigmoid'))
How about this model?#
Optimal activation function?#
For output layers:#
Source on optimal activation function can be found here