Classifier in Keras

blairchen

Publish：Aug 22, 2019

Classifier in Keras

data preprocessing

from keras.datasets import mnist

# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# X shape (60,000 28x28), y shape (10,000, )
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# data pre-processing
X_train = X_train.reshape(X_train.shape[0], -1) / 255.   # normalize
X_test = X_test.reshape(X_test.shape[0], -1) / 255.      # normalize

y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

print(X_train[1].shape)
"""
(784,)
"""

#print(y_train[:3])
"""
[[ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.]
 [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]]
"""

1. build model

import numpy as np

np.random.seed(1337)  # for reproducibility

from keras.datasets import mnist

from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import RMSprop

Another way to build your neural net

# Another way to build your neural net
model = Sequential([
    Dense(32, input_dim=784),
    Activation('relu'),
    Dense(10),
    Activation('softmax'),
])
model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_5 (Dense)              (None, 32)                25120     
_________________________________________________________________
activation_5 (Activation)    (None, 32)                0         
_________________________________________________________________
dense_6 (Dense)              (None, 10)                330       
_________________________________________________________________
activation_6 (Activation)    (None, 10)                0         
=================================================================
Total params: 25,450
Trainable params: 25,450
Non-trainable params: 0
_________________________________________________________________

1 2	# Another way to define your optimizer rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)

2. compile model

# We add metrics to get more results you want to see
model.compile(optimizer=rmsprop,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

3. train model

print('Training ------------')
# Another way to train the model
model.fit(X_train, y_train, epochs=2, batch_size=32)

# """
# Training ------------
# Epoch 1/2
# 60000/60000 [==============================] - 2s - loss: 0.3506 - acc: 0.9025     
# Epoch 2/2
# 60000/60000 [==============================] - 2s - loss: 0.1995 - acc: 0.9421   
# """

4. evaluate model

print('\nTesting ------------')
# Evaluate the model with the metrics we defined earlier
loss, accuracy = model.evaluate(X_test, y_test)

print('test loss: ', loss)
print('test accuracy: ', accuracy)

"""
Testing ------------
 9760/10000 [============================>.] - ETA: 0s

test loss:  0.1724540345
test accuracy:  0.9489
"""