How can I input data into keras? What is the structure? Specifically what is the x_train and y_train if I have more than 2 columns?
This is the data I want to input:
I am trying to define Xtrain in this example Multi Layer Perceptron Neural Network code Keras has in its documentation. (http://keras.io/examples/) Here is the code:
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
model = Sequential()
model.add(Dense(64, input_dim=20, init='uniform'))
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(64, init='uniform'))
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(2, init='uniform'))
model.add(Activation('softmax'))
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer=sgd)
model.fit(X_train, y_train, nb_epoch=20, batch_size=16)
score = model.evaluate(X_test, y_test, batch_size=16)
EDIT (additional information):
Looking here: What is data type for Python Keras deep learning package?
Keras uses numpy arrays containing the theano.config.floatX floating point type. This can be configured in your .theanorc file. Typically, it will be float64 for CPU computations and float32 for GPU computations, although you can also set it to float32 when working on the CPU if you prefer. You can create a zero-filled array of the proper type by the command
X = numpy.zeros((4,3), dtype=theano.config.floatX)
Question: Step 1 looks like create a floating point numpy array using my above data from the excel file. What do I do with the winner column?
The input shape In Keras, the input layer itself is not a layer, but a tensor. It's the starting tensor you send to the first hidden layer. This tensor must have the same shape as your training data. Example: if you have 30 images of 50x50 pixels in RGB (3 channels), the shape of your input data is (30,50,50,3) .
It is generally recommend to use the Keras Functional model via Input , (which creates an InputLayer ) without directly using InputLayer . When using InputLayer with the Keras Sequential model, it can be skipped by moving the input_shape parameter to the first layer after the InputLayer .
In neural networks, a hidden layer is located between the input and output of the algorithm, in which the function applies weights to the inputs and directs them through an activation function as the output. In short, the hidden layers perform nonlinear transformations of the inputs entered into the network.
Keras can separate a portion of your training data into a validation dataset and evaluate the performance of your model on that validation dataset in each epoch. You can do this by setting the validation_split argument on the fit() function to a percentage of the size of your training dataset.
It all depends on your need.
It looks like that you want to predict the winner based on the parameters shown in column A - N. Then you should define input_dim
to be 14, and X_train
should be an (N,14) numpy array like this:
[
[9278, 37.9, ...],
[18594, 36.3, ...],
...
]
It seems that your prediction set only contains 2 items ( 2 president candidates LOL), so you should encode the answer Y_train
in an (N,2) numpy array like this:
[
[1, 0],
[1, 0],
...
[0, 1],
[0, 1],
...
]
where [1,0]
indicates that Barack Obama is the winner and vice versa.
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