### MNIST Softmax Visualization 2

#### by allenlu2007

Ref: http://qiita.com/oimou/items/4a4258a7f7cc2bd70afe

同前文同樣用 MNIST with softmax classifier. 但不同的 reference paper.

**In summary: imshow (original image), plot(loss), imshow(weights)**

主要的差異是前文是用 GD, 本文用 SGD.

```
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
```

```
import tensorflow as tf
```

```
x = tf.placeholder(tf.float32, [None, 784])
```

```
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
```

```
y = tf.nn.softmax(tf.matmul(x, W) + b)
```

```
y_ = tf.placeholder(tf.float32, [None, 10])
```

```
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
```

```
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)
```

```
sess = tf.InteractiveSession()
```

```
tf.global_variables_initializer().run()
```

```
import matplotlib.pyplot as plt
import numpy as np
plt.imshow(mnist.train.images[0].reshape([28, 28]))
plt.gray()
```

```
n_train = 10000
n_batch = 100
# for visualization
fig, ax = plt.subplots(1, 1, figsize=(15, 5))
xvalues = np.arange(n_train)
yvalues = np.zeros(n_train)
lines, = ax.plot(xvalues, yvalues, label='cross_entropy')
for i in range(n_train):
batch_xs, batch_ys = mnist.train.next_batch(n_batch)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
yvalues[i] = cross_entropy.eval(feed_dict={x: mnist.test.images[0:100], y_: mnist.test.labels[0:100]})
lines.set_data(xvalues, yvalues)
ax.set_ylim((yvalues.min(), yvalues.max()))
ax.set_ylim((yvalues.min(), 0.3))
plt.legend()
plt.pause(.00001)
```

```
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
```

```
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
```

```
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
```

```
w = W.eval().T
fig = plt.figure(figsize=(10, 4))
for i in range(10):
ax = fig.add_subplot(2, 5, i + 1)
ax.imshow(w[i].reshape([28, 28]), cmap="seismic")
```

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