In [1]:
from IPython.core.display import display, HTML
display(HTML("<style> .container{width:100% !important;}</style>"))
In [2]:
import tensorflow as tf
import warnings
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (12, 8)
In [3]:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./mnist/data/", one_hot=True)
In [4]:
global_step = tf.Variable(0, trainable=False, name="global_step")
X = tf.placeholder(tf.float32, shape=[None, 784], name="X") # None, 784
Y = tf.placeholder(tf.float32, shape=[None, 10], name="Y")
W1 = tf.Variable(tf.random_normal([784, 256], mean=0, stddev=0.01), name="W1")
W2 = tf.Variable(tf.random_normal([256, 256], mean=0, stddev=0.01), name="W2")
W3 = tf.Variable(tf.random_normal([256, 10], mean=0, stddev=0.011), name="W3")
b1 = tf.zeros([256], name="bias1")
b2 = tf.zeros([256], name="bias2")
b3 = tf.zeros([10], name="bias3")
In [5]:
keep_prob = tf.placeholder(tf.float32)
with tf.name_scope("layer1"):
L1 = tf.add(tf.matmul(X, W1), b1)
L1 = tf.nn.relu(L1)
L1 = tf.nn.dropout(L1, keep_prob)
with tf.name_scope("layer2"):
L2 = tf.add(tf.matmul(L1, W2), b2)
L2 = tf.nn.relu(L2)
L2 = tf.nn.dropout(L2, keep_prob)
with tf.name_scope("layer3"):
model = tf.add(tf.matmul(L2, W3), b3)
In [6]:
with tf.name_scope("optimizer"):
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(labels=Y, logits=model))
opt = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost, global_step=global_step)
tf.summary.scalar("cost", cost)
In [7]:
init = tf.global_variables_initializer()
sess = tf.Session()
# saver = tf.train.Saver(tf.global_variables())
sess.run(init)
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter("./logs/mnist_dropout", sess.graph)
In [8]:
batch_size = 50
total_batch = int(mnist.train.num_examples/batch_size)
cost_epoch = []
In [9]:
%%time
for epoch in range(30):
total_cost = 0
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
_, cost_val = sess.run([opt, cost], feed_dict={X:batch_xs, Y:batch_ys, keep_prob: 0.8})
total_cost += cost_val
cost_epoch.append(total_cost)
summary = sess.run(merged, feed_dict={X:batch_xs, Y:batch_ys, keep_prob: 0.8})
writer.add_summary(summary, global_step=sess.run(global_step))
print("epoch: {}, Avg.cost: {}".format(epoch+1, total_cost / total_batch))
In [10]:
plt.plot(cost_epoch, "g")
plt.title("cost_epoch")
plt.show()
In [11]:
is_correct = tf.equal(tf.argmax(model, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
print("accuracy: {}".format(sess.run(accuracy, feed_dict={X: mnist.test.images,
Y: mnist.test.labels,
keep_prob: 1})))
In [12]:
### tensorboard graph
from IPython.display import clear_output, Image, display, HTML
def strip_consts(graph_def, max_const_size=32):
"""Strip large constant values from graph_def."""
strip_def = tf.GraphDef()
for n0 in graph_def.node:
n = strip_def.node.add()
n.MergeFrom(n0)
if n.op == 'Const':
tensor = n.attr['value'].tensor
size = len(tensor.tensor_content)
if size > max_const_size:
tensor.tensor_content = "<stripped %d bytes>"%size
return strip_def
def show_graph(graph_def, max_const_size=32):
"""Visualize TensorFlow graph."""
if hasattr(graph_def, 'as_graph_def'):
graph_def = graph_def.as_graph_def()
strip_def = strip_consts(graph_def, max_const_size=max_const_size)
code = """
<script>
function load() {{
document.getElementById("{id}").pbtxt = {data};
}}
</script>
<link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>
<div style="height:600px">
<tf-graph-basic id="{id}"></tf-graph-basic>
</div>
""".format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))
iframe = """
<iframe seamless style="width:1200px;height:620px;border:0" srcdoc="{}"></iframe>
""".format(code.replace('"', '"'))
display(HTML(iframe))
In [13]:
show_graph(tf.get_default_graph().as_graph_def())
In [14]:
# ### tensorboard
# def TB(cleanup=False):
# import webbrowser
# webbrowser.open('http://127.0.0.1:6006')
# !tensorboard --logdir="./logs/mnist_dropout/"
# TB()
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