9.1 CNN for MNIST with TensorFlow and Keras
import os
import numpy as np
np.random.seed(123)
print("NumPy:{}".format(np.__version__))
import tensorflow as tf
tf.set_random_seed(123)
print("TensorFlow:{}".format(tf.__version__))
NumPy:1.13.1
TensorFlow:1.4.1
DATASETSLIB_HOME = '../datasetslib'
import sys
if not DATASETSLIB_HOME in sys.path:
sys.path.append(DATASETSLIB_HOME)
%reload_ext autoreload
%autoreload 2
import datasetslib
datasetslib.datasets_root = os.path.join(os.path.expanduser('~'),'datasets')
Get the MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(os.path.join(datasetslib.datasets_root,'mnist'), one_hot=True)
X_train = mnist.train.images
X_test = mnist.test.images
Y_train = mnist.train.labels
Y_test = mnist.test.labels
Extracting /home/armando/datasets/mnist/train-images-idx3-ubyte.gz
Extracting /home/armando/datasets/mnist/train-labels-idx1-ubyte.gz
Extracting /home/armando/datasets/mnist/t10k-images-idx3-ubyte.gz
Extracting /home/armando/datasets/mnist/t10k-labels-idx1-ubyte.gz
CNN with TensorFlow for MNIST Data
tf.reset_default_graph()
n_classes = 10
n_width = 28
n_height = 28
n_depth = 1
n_inputs = n_height * n_width * n_depth
learning_rate = 0.001
n_epochs = 10
batch_size = 100
n_batches = int(mnist.train.num_examples/batch_size)
x = tf.placeholder(dtype=tf.float32, name="x", shape=[None, n_inputs])
y = tf.placeholder(dtype=tf.float32, name="y", shape=[None, n_classes])
x_ = tf.reshape(x, shape=[-1, n_width, n_height, n_depth])
layer1_w = tf.Variable(tf.random_normal(shape=[4,4,n_depth,32],
stddev=0.1),
name='l1_w')
layer1_b = tf.Variable(tf.random_normal([32]),
name='l1_b')
layer1_conv = tf.nn.relu(tf.nn.conv2d(x_,
layer1_w,
strides=[1,1,1,1],
padding='SAME'
) +
layer1_b
)
layer1_pool = tf.nn.max_pool(layer1_conv,
ksize=[1,2,2,1],
strides=[1,2,2,1],
padding='SAME'
)
layer2_w = tf.Variable(tf.random_normal(shape=[4,4,32,64],
stddev=0.1),
name='l2_w')
layer2_b = tf.Variable(tf.random_normal([64]),
name='l2_b')
layer2_conv = tf.nn.relu(tf.nn.conv2d(layer1_pool,
layer2_w,
strides=[1,1,1,1],
padding='SAME'
) +
layer2_b
)
layer2_pool = tf.nn.max_pool(layer2_conv,
ksize=[1,2,2,1],
strides=[1,2,2,1],
padding='SAME'
)
layer3_w = tf.Variable(tf.random_normal(shape=[64*7*7*1,1024],
stddev=0.1),
name='l3_w')
layer3_b = tf.Variable(tf.random_normal([1024]),
name='l3_b')
layer3_fc = tf.nn.relu(tf.matmul(tf.reshape(layer2_pool,
[-1, 64*7*7*1]),
layer3_w) +
layer3_b
)
layer4_w = tf.Variable(tf.random_normal(shape=[1024, n_classes],
stddev=0.1),
name='l4_w'
)
layer4_b = tf.Variable(tf.random_normal([n_classes]),name='l4_b')
layer4_out = tf.matmul(layer3_fc,layer4_w)+layer4_b
model = layer4_out
entropy = tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=y)
loss = tf.reduce_mean(entropy)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
with tf.Session() as tfs:
tf.global_variables_initializer().run()
for epoch in range(n_epochs):
total_loss = 0.0
for batch in range(n_batches):
batch_x,batch_y = mnist.train.next_batch(batch_size)
feed_dict={x:batch_x, y: batch_y}
batch_loss,_ = tfs.run([loss, optimizer],
feed_dict=feed_dict
)
total_loss += batch_loss
average_loss = total_loss / n_batches
print("Epoch: {0:04d} loss = {1:0.6f}".format(epoch,average_loss))
print("Model Trained.")
predictions_check = tf.equal(tf.argmax(model,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(predictions_check, tf.float32))
feed_dict = {x:mnist.test.images, y:mnist.test.labels}
print("Accuracy:", accuracy.eval(feed_dict=feed_dict))
Epoch: 0000 loss = 2.142813
Epoch: 0001 loss = 0.108121
Epoch: 0002 loss = 0.077320
Epoch: 0003 loss = 0.062054
Epoch: 0004 loss = 0.050821
Epoch: 0005 loss = 0.044723
Epoch: 0006 loss = 0.034373
Epoch: 0007 loss = 0.030643
Epoch: 0008 loss = 0.029777
Epoch: 0009 loss = 0.024144
Model Trained.
Accuracy: 0.9854
CNN with Keras for MNIST Data
import keras
from keras.models import Sequential
from keras.layers import Conv2D,MaxPooling2D, Dense, Flatten, Reshape
from keras.optimizers import SGD
Using TensorFlow backend.
tf.reset_default_graph()
keras.backend.clear_session()
n_filters=[32,64]
learning_rate = 0.01
n_epochs = 10
batch_size = 100
model = Sequential()
model.add(Reshape(target_shape=(n_width,n_height,n_depth),
input_shape=(n_inputs,)
)
)
model.add(Conv2D(filters=n_filters[0],
kernel_size=4,
padding='SAME',
activation='relu'
)
)
model.add(MaxPooling2D(pool_size=(2,2),
strides=(2,2)
)
)
model.add(Conv2D(filters=n_filters[1],
kernel_size=4,
padding='SAME',
activation='relu',
)
)
model.add(MaxPooling2D(pool_size=(2,2),
strides=(2,2)
)
)
model.add(Flatten())
model.add(Dense(units=1024, activation='relu'))
model.add(Dense(units=n_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer=SGD(lr=learning_rate),
metrics=['accuracy'])
model.fit(X_train, Y_train,
batch_size=batch_size,
epochs=n_epochs)
score = model.evaluate(X_test, Y_test)
print('\nTest loss:', score[0])
print('Test accuracy:', score[1])
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
reshape_1 (Reshape) (None, 28, 28, 1) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 28, 28, 32) 544
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 14, 14, 64) 32832
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 3136) 0
_________________________________________________________________
dense_1 (Dense) (None, 1024) 3212288
_________________________________________________________________
dense_2 (Dense) (None, 10) 10250
=================================================================
Total params: 3,255,914
Trainable params: 3,255,914
Non-trainable params: 0
_________________________________________________________________
Epoch 1/10
55000/55000 [==============================] - 5s 86us/step - loss: 0.9424 - acc: 0.7545
Epoch 2/10
55000/55000 [==============================] - 5s 86us/step - loss: 0.2490 - acc: 0.9253
Epoch 3/10
55000/55000 [==============================] - 5s 84us/step - loss: 0.1760 - acc: 0.9473
Epoch 4/10
55000/55000 [==============================] - 5s 83us/step - loss: 0.1368 - acc: 0.9597
Epoch 5/10
55000/55000 [==============================] - 4s 81us/step - loss: 0.1125 - acc: 0.9661
Epoch 6/10
55000/55000 [==============================] - 5s 86us/step - loss: 0.0961 - acc: 0.9712
Epoch 7/10
55000/55000 [==============================] - 5s 85us/step - loss: 0.0842 - acc: 0.9752
Epoch 8/10
55000/55000 [==============================] - 4s 81us/step - loss: 0.0751 - acc: 0.9778
Epoch 9/10
55000/55000 [==============================] - 5s 82us/step - loss: 0.0686 - acc: 0.9800
Epoch 10/10
55000/55000 [==============================] - 5s 90us/step - loss: 0.0623 - acc: 0.9811
10000/10000 [==============================] - 1s 57us/step
Test loss: 0.0613170108855
Test accuracy: 0.9796