keras_MNIST.py

# coding: utf-8

# In[1]:

from ds_utils.imports import *

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


# In[2]:

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


# In[3]:

X_train.shape 


# In[4]:

X_train[0][0].shape


# In[5]:

plt.imshow(X_train[0], cmap=plt.cm.gray_r)


# In[6]:

y_train[0]


# In[7]:

# 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)


# In[8]:

X_train.shape


# In[9]:

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255


# In[10]:

plt.imshow(X_train[0][0], cmap=plt.cm.gray_r)


# In[11]:

# 6. Preprocess class labels
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)


# In[12]:

Y_train.shape


# In[13]:

Y_train[:2]


# In[14]:

# 7. Define model architecture
model = Sequential()

model.add(
    Convolution2D(
        32, (3, 3), activation='relu', input_shape=(1, 28, 28)))


# In[15]:

model.output_shape


# In[16]:

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'))


# In[17]:

model.summary()


# In[18]:

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


# In[19]:

# 9. Fit model on training data
model.fit(X_train, Y_train, 
          batch_size=32, nb_epoch=1, verbose=1)


# In[20]:

# 10. Evaluate model on test data
score = model.evaluate(X_test, Y_test, verbose=0)


# In[21]:

score


# References
# - https://elitedatascience.com/keras-tutorial-deep-learning-in-python