keras1.ipynb

Kernel: Python 2 (SageMath)

'''Trains a simple deep NN on the MNIST dataset.   
Gets to 98.40% test accuracy after 20 epochs 
(there is *a lot* of margin for parameter tuning). 
2 seconds per epoch on a K520 GPU. 
''' 

from __future__ import print_function 
import numpy as np 
np.random.seed(1337)  # for reproducibility 

from keras.datasets import mnist 
from keras.models import Sequential 
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD, Adam, RMSprop 
from keras.utils import np_utils 

batch_size = 10
nb_classes = 10 
nb_epoch = 1

# the data, shuffled and split between train and test sets 
(X_train, y_train), (X_test, y_test) = mnist.load_data() 
 
X_train = X_train.reshape(60000, 784) 
X_test = X_test.reshape(10000, 784) 
X_train = X_train.astype('float32') 
X_test = X_test.astype('float32') 
X_train /= 255 
X_test /= 255 
print(X_train.shape[0], 'train samples') 
print(X_test.shape[0], 'test samples') 
 
 
# convert class vectors to binary class matrices 
Y_train = np_utils.to_categorical(y_train, nb_classes) 
Y_test = np_utils.to_categorical(y_test, nb_classes) 
 
model = Sequential() 



model.add(Dense(100, input_shape=(784,))) 
model.add(Activation('sigmoid')) 
model.add(Dense(input_dim =100, output_dim =10))
model.add(Activation('softmax'))

sgd = SGD(lr=0.1, decay=0, momentum=0.0, nesterov=False)



model.summary() 


#D, H, C = 1000, 100, 10
#model.add(Dense(input_dim =D, output_dim = H))
# model.add(Dense(input_dim =H, output_dim = C))


model.compile(loss='categorical_crossentropy', 
               optimizer=sgd, 
               metrics=['accuracy']) 
 
history = model.fit(X_train, Y_train, 
                     batch_size=batch_size, nb_epoch=nb_epoch, 
                     verbose=0, validation_data=(X_test, Y_test)) 
score = model.evaluate(X_test, Y_test, verbose=0) 
print('Test score:', score[0]) 
print('Test accuracy:', score[1]) 

Using TensorFlow backend.

60000 train samples
10000 test samples
____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
dense_1 (Dense)                  (None, 100)           78500       dense_input_1[0][0]              
____________________________________________________________________________________________________
activation_1 (Activation)        (None, 100)           0           dense_1[0][0]                    
____________________________________________________________________________________________________
dense_2 (Dense)                  (None, 10)            1010        activation_1[0][0]               
____________________________________________________________________________________________________
activation_2 (Activation)        (None, 10)            0           dense_2[0][0]                    
====================================================================================================
Total params: 79,510
Trainable params: 79,510
Non-trainable params: 0
____________________________________________________________________________________________________
Test score: 0.227122732595
Test accuracy: 0.9333