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%matplotlib inline



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import numpy as np



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from sklearn import datasets

Keras Hello World

Install Keras

https://keras.io/#installation

Install dependencies

Install TensorFlow backend: https://www.tensorflow.org/install/

pip install tensorflow

Insall h5py (required if you plan on saving Keras models to disk): http://docs.h5py.org/en/latest/build.html#wheels

pip install h5py

Install pydot (used by visualization utilities to plot model graphs): https://github.com/pydot/pydot#installation

pip install pydot

Install Keras

pip install keras

Import packages and check versions



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import tensorflow as tf
tf.__version__



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import keras
keras.__version__



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import h5py
h5py.__version__



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import pydot
pydot.__version__



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from keras.models import Sequential

model = Sequential()



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from keras.layers import Dense

model.add(Dense(units=6, activation='relu', input_dim=4))
model.add(Dense(units=3, activation='softmax'))



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from keras.utils import plot_model

plot_model(model, show_shapes=True, to_file="model.png")



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model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])



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iris = datasets.load_iris()

TODO: shuffle



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x_train = iris.data



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y_train = np.zeros(shape=[x_train.shape[0], 3])



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y_train[(iris.target == 0), 0] = 1
y_train[(iris.target == 1), 1] = 1
y_train[(iris.target == 2), 2] = 1



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x_test = x_train
y_test = y_train



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model.fit(x_train, y_train)



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model.evaluate(x_test, y_test)



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model.predict(x_test)