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Keras Example on Jupyter (works on old version of Keras, not current version)
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# Python 2.7 on Jupyter | |
# Libraries: Keras, pandas, numpy, matplotlib, seaborn | |
# For compatibility | |
from __future__ import absolute_import | |
from __future__ import print_function | |
# For manipulating data | |
import pandas as pd | |
import numpy as np | |
from keras.utils import np_utils # For y values | |
# For plotting | |
%matplotlib inline | |
import seaborn as sns | |
# For Keras | |
from keras.models import Sequential | |
from keras.layers.core import Dense, Dropout | |
# Set data | |
data = np.array([ | |
[0, 0, 0], | |
[1, 1, 0], | |
[2, 2, 0], | |
[3, 3, 0], | |
[4, 4, 0], | |
[5, 5, 1], | |
[6, 6, 1], | |
[7, 7, 1], | |
[8, 8, 1], | |
[9, 9, 1], | |
]) | |
data = np.vstack((data, data, data, data)) # Just for sufficient input | |
data = pd.DataFrame(data, columns=['x', 'y', 'class']) | |
# Split X and y | |
X = data.iloc[:, :-1].values | |
y = data.iloc[:, -1:].values | |
# Get dimensions of input and output | |
dimof_input = X.shape[1] | |
dimof_output = np.max(y) + 1 | |
print('dimof_input: ', dimof_input) | |
print('dimof_output: ', dimof_output) | |
# Set y categorical | |
y = np_utils.to_categorical(y, dimof_output) | |
# Set constants | |
batch_size = 128 | |
dimof_middle = 100 | |
dropout = 0.2 | |
countof_epoch = 100 | |
verbose = 0 | |
print('batch_size: ', batch_size) | |
print('dimof_middle: ', dimof_middle) | |
print('dropout: ', dropout) | |
print('countof_epoch: ', countof_epoch) | |
print('verbose: ', verbose) | |
print() | |
# Set model | |
model = Sequential() | |
model.add(Dense(dimof_middle, input_dim=dimof_input, init='uniform', activation='tanh')) | |
model.add(Dropout(dropout)) | |
model.add(Dense(dimof_middle, init='uniform', activation='tanh')) | |
model.add(Dropout(dropout)) | |
model.add(Dense(dimof_output, init='uniform', activation='softmax')) | |
model.compile(loss='mse', optimizer='sgd', metrics=['accuracy']) | |
# Train | |
model.fit( | |
X, y, | |
validation_split=0.2, | |
batch_size=batch_size, nb_epoch=countof_epoch, verbose=verbose) | |
# Evaluate | |
loss, accuracy = model.evaluate(X, y, verbose=verbose) | |
print('loss: ', loss) | |
print('accuracy: ', accuracy) | |
print() | |
# Predict | |
# model.predict_classes(X, verbose=verbose) | |
print('prediction of [1, 1]: ', model.predict_classes(np.array([[1, 1]]), verbose=verbose)) | |
print('prediction of [8, 8]: ', model.predict_classes(np.array([[8, 8]]), verbose=verbose)) | |
# Plot | |
sns.lmplot('x', 'y', data, 'class', fit_reg=False).set(title='Data') | |
data_ = data.copy() | |
data_['class'] = model.predict_classes(X, verbose=0) | |
sns.lmplot('x', 'y', data_, 'class', fit_reg=False).set(title='Trained Result') | |
data_['class'] = [ 'Error' if is_error else 'Non Error' for is_error in data['class'] != data_['class']] | |
sns.lmplot('x', 'y', data_, 'class', fit_reg=False).set(title='Errors') | |
None |
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