Add test model training script

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Maximiliana Behnke 2016-09-14 14:27:08 +02:00
parent 76cda34544
commit 2c39bad1d6

91
scripts/train_test_model.py Executable file
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#!/usr/bin/env python
import sys
import os
import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
def softmax(x):
return np.exp(x) / np.sum(np.exp(x), axis=1)[:, None]
def baseline_model(pixels_count, classes_count):
model = Sequential()
# model.add(Dense(pixels_count, input_dim=pixels_count, init='normal', activation='relu'))
model.add(Dense(classes_count, input_dim=pixels_count, init='normal', activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
if __name__ == "__main__":
### Load trainset from mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
### Flatten pictures into vectors
pixels_count = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape(X_train.shape[0], pixels_count).astype('float32')
print "X shape: ", X_train.shape
X_test = X_test.reshape(X_test.shape[0], pixels_count).astype('float32')
### Normalize data to (0, 1)
X_train = X_train / 255
X_test = X_test / 255
### Change classes to one hot encoding matrixes
y_train = np_utils.to_categorical(y_train)
classes_count = y_train.shape[1]
print "Y shape: ", y_train.shape
y_test = np_utils.to_categorical(y_test)
# Train weight matrix
# Build the model
model = baseline_model(pixels_count, classes_count)
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), nb_epoch=10, batch_size=200, verbose=2)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
### Weight and bias matrixes - we extract them from the model
# weights_ones = np.ones((pixels_count, classes_count))
# print weights_ones.shape
weights, bias = model.get_weights()
print weights.shape
print bias.shape
print bias
### We calculate lr using softmax!
dot_out = np.dot(X_train, weights)
print "dot_out shape: ", dot_out.shape
# print dot_out[:10]
add_out = np.add(bias, dot_out)
print "add_out shape: ", add_out.shape
# print add_out[:10]
# lr = np.around(softmax(add_out), decimals = 6)
lr = softmax(add_out)
print "lr shape: ", lr.shape
# print lr[:10]
# print np.count_nonzero(lr)i
### Save model to npz files
if not os.path.exists("test_model"):
os.makedirs("test_model")
np.savez("test_model/model", weights = weights, bias = bias)
print "Model saved! Check test_model directory"