implement a deep neural network to solve a classification problem.
Budget: $30 – $250 USD
implement a deep neural network to solve a classification problem.
Key requirements:
A-Build an MLP network with 8 neurons in the first hidden layer, 4 neurons each in the second and third hidden layers. Use 6 neurons for the output layer. All the hidden layer neurons use ReLU activation function. The output layer neurons use softmax activation function. The network should be compiled using Adam optimizer, categorical_crossentropy loss function and accuracy as a metric.
B-Train the compiled network in 10 epochs, with a batch size of 32. Use the encoded training set for training (utilize Helper function 2).
C-Complete the function predict_point below that should predict the region of any given point (x, y). Your result must be decoded with a threshold of 0.3.
While this is a one hour project, I'm looking for someone with strong Python skills and experience with deep learning and text data. Let's get this done!
Key requirements:
A-Build an MLP network with 8 neurons in the first hidden layer, 4 neurons each in the second and third hidden layers. Use 6 neurons for the output layer. All the hidden layer neurons use ReLU activation function. The output layer neurons use softmax activation function. The network should be compiled using Adam optimizer, categorical_crossentropy loss function and accuracy as a metric.
B-Train the compiled network in 10 epochs, with a batch size of 32. Use the encoded training set for training (utilize Helper function 2).
C-Complete the function predict_point below that should predict the region of any given point (x, y). Your result must be decoded with a threshold of 0.3.
While this is a one hour project, I'm looking for someone with strong Python skills and experience with deep learning and text data. Let's get this done!