tensor.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 Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.datasets import mnist

#%%
# 4. Load pre-shuffled MNIST data into train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train.shape

#%%
# 5. Preprocess input data
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
 
# 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(
    3, 3,
    activation='relu', 
    input_shape=(1,28,28)
))

#%%
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
 
#%%
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

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

#%%
# 9. Fit model on training data
model.fit(X_train, Y_train, 
          batch_size=32, nb_epoch=10, verbose=1)
 
# 10. Evaluate model on test data
score = model.evaluate(X_test, Y_test, verbose=0)