keras

Author: lazyprogrammer

ann_class2/keras_example.py

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+# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow
+# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow
+from __future__ import print_function, division
+from builtins import range
+# Note: you may need to update your version of future
+# sudo pip install -U future
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation
+from util import get_normalized_data, y2indicator
+
+import matplotlib.pyplot as plt
+
+# NOTE: do NOT name your file keras.py because it will conflict
+# with importing keras
+
+# installation is easy! just the usual "sudo pip(3) install keras"
+
+
+# get the data, same as Theano + Tensorflow examples
+# no need to split now, the fit() function will do it
+X, Y = get_normalized_data()
+
+# get shapes
+N, D = X.shape
+K = len(set(Y))
+
+# by default Keras wants one-hot encoded labels
+# there's another cost function we can use
+# where we can just pass in the integer labels directly
+# just like Tensorflow / Theano
+Y = y2indicator(Y)
+
+
+# the model will be a sequence of layers
+model = Sequential()
+
+
+# ANN with layers [784] -> [500] -> [300] -> [10]
+model.add(Dense(units=500, input_dim=D))
+model.add(Activation('relu'))
+model.add(Dense(units=300)) # don't need to specify input_dim
+model.add(Activation('relu'))
+model.add(Dense(units=K))
+model.add(Activation('softmax'))
+
+
+# list of losses: https://keras.io/losses/
+# list of optimizers: https://keras.io/optimizers/
+# list of metrics: https://keras.io/metrics/
+model.compile(
+  loss='categorical_crossentropy',
+  optimizer='adam',
+  metrics=['accuracy']
+)
+
+# note: multiple ways to choose a backend
+# either theano, tensorflow, or cntk
+# https://keras.io/backend/
+
+
+# gives us back a <keras.callbacks.History object at 0x112e61a90>
+r = model.fit(X, Y, validation_split=0.33, epochs=15, batch_size=32)
+print("Returned:", r)
+
+# print the available keys
+# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])
+print(r.history.keys())
+
+# plot some data
+plt.plot(r.history['loss'], label='loss')
+plt.plot(r.history['val_loss'], label='val_loss')
+plt.legend()
+plt.show()
+
+# accuracies
+plt.plot(r.history['acc'], label='acc')
+plt.plot(r.history['val_acc'], label='val_acc')
+plt.legend()
+plt.show()
+
+