mnist ~3800 parameters model

mnist.4k.py

# 3. Import libraries and modules
import numpy as np
np.random.seed(123)  # for reproducibility
 
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution1D, Convolution2D, MaxPooling1D,MaxPooling2D,AveragePooling2D,AveragePooling1D, BatchNormalization
from keras.utils import np_utils
from keras.datasets import mnist
from keras.layers.core import Reshape
 
from matplotlib import pyplot as plt

#from keras.utils.vis_utils import plot_model


# 4. Load pre-shuffled MNIST data into train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
 
orig_x_test = X_test
# 5. Preprocess input data
X_train = X_train.reshape(X_train.shape[0], 28,28,1)
X_test = X_test.reshape(X_test.shape[0], 28,28,1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255


# plt.imshow(X_train[0])
# plt.show()


print ("X_train shape1", X_train[0].shape)
 
# 6. Preprocess class labels
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)
 
#7. Define model architecture
model = Sequential()

model.add(Convolution2D(20, (5, 5) , activation='relu', input_shape=(28,28,1)))
model.add(BatchNormalization(momentum=0.1))
model.add(Dropout(0.1))
model.add(AveragePooling2D(2))
model.add(Convolution2D(10,(1,1) , activation='relu'))
model.add(BatchNormalization(momentum=0.1))
model.add(Dropout(0.1))
model.add(AveragePooling2D(2))
model.add(Convolution2D(12,(3,3) , activation='relu'))
model.add(BatchNormalization(momentum=0.1))
model.add(Dropout(0.1))
model.add(Convolution2D(10,(1,1) , activation='relu'))
model.add(Dropout(0.1))

model.add(Dense(10, activation='relu'))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))

# 8. Compile model
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
model.summary()

#plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
 
#print ("X_train shape2", X_train.shape)

# 9. Fit model on training data
# history = model.fit(X_train, Y_train, 
#           batch_size=512, nb_epoch=20, verbose=1,shuffle=True,
#           validation_data=(X_test, Y_test))

# history = model.fit(X_train, Y_train, 
#           batch_size=128, nb_epoch=10, verbose=1,shuffle=True,
#           validation_data=(X_test, Y_test))
 
history = model.fit(X_train, Y_train, 
          batch_size=8, nb_epoch=80, verbose=1,shuffle=True,
          validation_data=(X_test, Y_test))

print ("history", history.history);

#plot history
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], label='test')
plt.legend()
#plt.savefig('training.png')
plt.show()

# 10. Evaluate model on test data
test_score = model.evaluate(X_test, Y_test, verbose=0)
print ("test score", test_score)
print("Test Large CNN Error: %.2f%%" % (100-test_score[1]*100))

train_score = model.evaluate(X_train, Y_train, verbose=0)
print ("train score", train_score)
print("Train Large CNN Error: %.2f%%" % (100-train_score[1]*100))



# prediction = model.predict(X_test[0:1])
# print("prediction", prediction)
# plt.imshow(orig_x_test[0])
# plt.show()