This notebok contains a walkthrough to help you get started with TensorFlow. We'll work on a few exercises for...
Of course, Deep Learning is a wide and rich field, and TensorFlow can do much more than the above. Here are some recent articles you can check out:
Before you begin, please make sure you have TensorFlow version 1.3.0rc0 (or higher) installed on your machine, where "rc" means "release candidate". We'll be working with the Datasets API, which we're currently developing to make it easy to efficiently train models on large amounts of data (say, that are too big to fit into memory).
Here are some short videos and a couple book recommendations.
You can follow TensorFlow on Twitter, if you like, at https://twitter.com/tensorflow
In [1]:
# Python 2 & 3 compatibility
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import os
import numpy as np
import pandas as pd
import pylab
%matplotlib inline
import tensorflow as tf
print('You are running TensorFlow version:', tf.__version__)
from tensorflow.examples.tutorials.mnist import input_data
You are running TensorFlow version: 1.2.1
In [2]:
def make_noisy_data(m=0.1, b=0.3, n=100):
x = np.random.rand(n).astype(np.float32)
noise = np.random.normal(scale=0.01, size=len(x))
y = m * x + b + noise
return x, y
Create training and testing data.
In [3]:
x_train, y_train = make_noisy_data()
x_test, y_test = make_noisy_data()
In [4]:
# Uncomment the following lines to plot the data.
# Isn't it beautiful?
# Training data is shown in blue, and testing data in green.
# pylab.plot(x_train, y_train, 'b.')
# pylab.plot(x_test, y_test, 'g.')
In [5]:
# The following line (as you might imagine) clears the graph.
# Why do we need it? Jupyter Notebooks maintain state.
# If you run this Notebook twice (and forget to reset it),
# this line will restore everything to a clean state for you.
tf.reset_default_graph()
In [6]:
# You can think of a Session as an 'execution environment' for a graph.
# We won't need this until we're ready to run the graph, but
# I'll create it now, just for kicks.
sess = tf.Session()
In [7]:
# Path to a log directory.
# As written, it will be created in the same directory as this notebook.
# Later, we'll use TensorBoard to visualize data stored
# in this directory - and it will be awesome.
# Tip:
# If you have trouble with TensorBoard, delete
# the log directory, and re-run the notebook.
LOGDIR = './graphs'
Define placeholders for data we'll feed to the graph.
In [8]:
# You can think of a 'Placeholder' as a promise. It's a value we
# promise to provide when we execute the graph.
# A lot of this code is for display purposes.
# - 'tf.name_scope' nests our placeholders under an 'input' block
# - name='x-input' gives TensorBoard a display name for this node.
# shape=[None] means x_placeholder is a one dimensional array of any length.
# - this is so we can feed a 'batch' of data later, for example,
# - for stochastic gradient descent, or to make predictions.
with tf.name_scope('input'):
x_placeholder = tf.placeholder(shape=[None], dtype=tf.float32, name='x-input')
y_placeholder = tf.placeholder(shape=[None], dtype=tf.float32, name='y-input')
In [9]:
with tf.name_scope('model'):
m = tf.Variable(tf.random_normal([1]), name='m')
b = tf.Variable(tf.random_normal([1]), name='b')
# This is the same as y = tf.add(tf.mul(m, x_placeholder), b),
# but looks nicer
y = m * x_placeholder + b
In [10]:
LEARNING_RATE = 0.5 # a magic number!
# as you gain experience with Deep Learning,
# you will become proficient in picking proper
# values (or just stop worrying about it)
with tf.name_scope('training'):
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.square(y - y_placeholder))
with tf.name_scope('optimizer'):
optimizer = tf.train.GradientDescentOptimizer(LEARNING_RATE)
train = optimizer.minimize(loss)
In [11]:
# Write the graph
writer = tf.summary.FileWriter(LOGDIR)
writer.add_graph(sess.graph)
# Attach summaries to Tensors (for TensorBoard visualization)
tf.summary.histogram('m', m)
tf.summary.histogram('b', b)
tf.summary.scalar('loss', loss)
# This op will calculate our summary data when run
summary_op = tf.summary.merge_all()
In [12]:
# Notice we're running this line with our session.
# All the TensorFlow code prior to this point has
# served to define the graph.
sess.run(tf.global_variables_initializer())
In [13]:
# 'm' and 'b' were initialized to random values
# let's see what these were
initial_vals = sess.run([m, b])
print ("Initial values for m: %f, b: %f" % (initial_vals[0], initial_vals[1]))
Initial values for m: 1.025586, b: -0.626024
In [14]:
TRAIN_STEPS = 201
for step in range(TRAIN_STEPS):
# Session will run two ops:
# - summary_op prepares summary data we'll write to disk in a moment
# - train will use the optimizer to adjust our variables to reduce loss
summary_result, _ = sess.run([summary_op, train],
feed_dict={x_placeholder: x_train,
y_placeholder: y_train})
# write the summary data to disk
writer.add_summary(summary_result, step)
# Uncomment the following two lines to watch training happen real time.
if step % 20 == 0:
vals = sess.run([m, b])
print("Step: %d, m: %f, b: %f" % (step, vals[0], vals[1]))
# close the writer when we're finished using it
writer.close()
Step: 0, m: 1.175373, b: -0.166666
Step: 20, m: 0.344110, b: 0.167180
Step: 40, m: 0.156737, b: 0.268489
Step: 60, m: 0.111893, b: 0.292735
Step: 80, m: 0.101160, b: 0.298538
Step: 100, m: 0.098592, b: 0.299927
Step: 120, m: 0.097977, b: 0.300259
Step: 140, m: 0.097830, b: 0.300339
Step: 160, m: 0.097795, b: 0.300358
Step: 180, m: 0.097786, b: 0.300362
Step: 200, m: 0.097784, b: 0.300364
In [15]:
# If things worked properly, 'm' should be about 0.1,
# and 'b' should be about 0.3
# (+/- a bit, because we added noise when we generated the data)
print ("Learned values for m: %f, b: %f" % (sess.run(m), sess.run(b)))
Learned values for m: 0.097784, b: 0.300364
In [16]:
# Use the trained model to make a prediction!
# Remember that x_placeholder must be a vector, hence [2] not just 2.
# We expect the result to be (about): 2 * 0.1 + 0.3 + noise ~= 0.5
sess.run(y, feed_dict={x_placeholder: [2]})
Out[16]:
array([ 0.49593186], dtype=float32)
Let's see what we got for all that work logging variables.
Start TensorBoard by running this command from a terminal.
$ tensorboard --logdir=graphs
Note: first cd into the directory that contains this notebook. If you are running TensorFlow in a virtualenv and you have opened a new terminal window, be sure to start the virtualenv again before running TensorBoard.
After you have run this command, open TensorBoard by pointing your browser to http://localhost:6006 Then, click on the tabs for 'scalars', 'distributions', 'histograms', and 'graphs' to learn more.
If you run into trouble, delete LOGDIR (to clear information from previous runs), then re-run this script, and restart TensorBoard.
In [17]:
tf.reset_default_graph()
sess = tf.Session()
In [18]:
# Import the MNIST dataset.
# It will be downloaded to '/tmp/data' if you don't already have a local copy.
mnist = input_data.read_data_sets('/tmp/data', one_hot=True)
Extracting /tmp/data/train-images-idx3-ubyte.gz
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz
In [19]:
# Uncomment these lines to understand the format of the dataset.
# 1. There are 55k, 5k, and 10k examples in train, validation, and test.
# print ('Train, validation, test: %d, %d, %d' %
# (len(mnist.train.images), len(mnist.validation.images), len(mnist.test.images)))
# 2. The format of the labels is 'one-hot'.
# The fifth image happens to be a '6'.
# This is represented as '[ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]'
# print (mnist.train.labels[4])
# You can find the index of the label, like this:
# print (np.argmax(mnist.train.labels[4]))
# 3. An image is a 'flattened' array of 28*28 = 784 pixels.
# print (len(mnist.train.images[4]))
# 4. To display an image, first reshape it to 28x28.
# pylab.imshow(mnist.train.images[4].reshape((28,28)), cmap=pylab.cm.gray_r)
# pylab.title('Label: %d' % np.argmax(mnist.train.labels[4]))
In [20]:
NUM_CLASSES = 10
NUM_PIXELS = 28 * 28
TRAIN_STEPS = 2000
BATCH_SIZE = 100
LEARNING_RATE = 0.5
In [21]:
# Define inputs
images = tf.placeholder(dtype=tf.float32, shape=[None, NUM_PIXELS])
labels = tf.placeholder(dtype=tf.float32, shape=[None, NUM_CLASSES])
In [22]:
# Define model
W = tf.Variable(tf.truncated_normal([NUM_PIXELS, NUM_CLASSES]))
b = tf.Variable(tf.zeros([NUM_CLASSES]))
y = tf.matmul(images, W) + b
In [23]:
# Define loss and optimizer
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=labels))
train_step = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(loss)
In [24]:
# Initialize variables after the model is defined
sess.run(tf.global_variables_initializer())
In [25]:
# Train the model
for i in range(TRAIN_STEPS):
batch_images, batch_labels = mnist.train.next_batch(BATCH_SIZE)
sess.run(train_step, feed_dict={images: batch_images, labels: batch_labels})
In [26]:
# Evaluate the trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Accuracy %f" % sess.run(accuracy, feed_dict={images: mnist.test.images,
labels: mnist.test.labels}))
Accuracy 0.894400
A method to make predictions on a single image
In [27]:
def predict(i):
image = mnist.test.images[i]
actual_label = np.argmax(mnist.test.labels[i])
prediction = tf.argmax(y,1)
predicted_label = sess.run(prediction, feed_dict={images: [image]})
print ("Predicted: %d, actual: %d" % (predicted_label, actual_label))
pylab.imshow(mnist.test.images[i].reshape((28,28)), cmap=pylab.cm.gray_r)
return predicted_label, actual_label
predict(5)
Predicted: 1, actual: 1
Out[27]:
(array([1]), 1)
Using the magic of automatic differentiation, we will now write a Deep Neural Network to classify handwritten digits. If this seems like a big jump from Linear and Logistic Regression - keep in mind, the goal of this exercise is to show you that the part of the code that does the "hard work" (training the model) is nearly identical. We need only change the code to specify the model (a stack of fully connected layers instead of y = Wx + b). Once that's done, we can train the DNN using TensorFlow in the same way we train the Linear / Logistic models.
In [28]:
tf.reset_default_graph()
sess = tf.Session()
In [29]:
# number of neurons in each hidden layer
HIDDEN1_SIZE = 500
HIDDEN2_SIZE = 250
NUM_CLASSES = 10
NUM_PIXELS = 28 * 28
# experiment with the nubmer of training steps to
# see the effect
TRAIN_STEPS = 2000
BATCH_SIZE = 100
# we're using a different learning rate than the previous
# notebook, and a new optimizer
LEARNING_RATE = 0.001
In [30]:
# Define inputs
with tf.name_scope('input'):
images = tf.placeholder(tf.float32, [None, NUM_PIXELS], name="pixels")
labels = tf.placeholder(tf.float32, [None, NUM_CLASSES], name="labels")
In [31]:
# Method to create a fully connected layer
def fc_layer(input, size_out, name="fc", activation=None):
with tf.name_scope(name):
size_in = int(input.shape[1])
w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name="weights")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="bias")
wx_plus_b = tf.matmul(input, w) + b
if activation: return activation(wx_plus_b)
return wx_plus_b
# The way we initialize variables has an affect on how quickly
# training converges. We may explore with different strategies later.
# w = tf.Variable(tf.truncated_normal(shape=[size_in, size_out], stddev=1.0 / math.sqrt(float(size_in))))
In [32]:
# Define the model
# First, we'll create two fully connected layers, with ReLU activations
fc1 = fc_layer(images, HIDDEN1_SIZE, "fc1", activation=tf.nn.relu)
fc2 = fc_layer(fc1, HIDDEN2_SIZE, "fc2", activation=tf.nn.relu)
# Next, we'll apply Dropout to the second layer
# This can help prevent overfitting, and I've added it here
# for illustration. You can comment this out, if you like.
dropped = tf.nn.dropout(fc2, keep_prob=0.9)
# Finally, we'll calculate logists. This will be
# the input to our Softmax function. Notice we
# don't apply an activation at this layer.
# If you've commented out the dropout layer,
# switch the input here to 'fc2'.
y = fc_layer(dropped, NUM_CLASSES, name="output")
In [33]:
# Define loss and an optimizer
with tf.name_scope("loss"):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=labels))
tf.summary.scalar('loss', loss)
with tf.name_scope("optimizer"):
# Whereas in the previous notebook we used a vanilla GradientDescentOptimizer
# here, we're using Adam. This is a single line of code change, and more
# importantly, TensorFlow will still automatically analyze our graph
# and determine how to adjust the variables to decrease the loss.
train = tf.train.AdamOptimizer(LEARNING_RATE).minimize(loss)
In [34]:
# Define evaluation
with tf.name_scope("evaluation"):
# these there lines are identical to the previous notebook.
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
In [35]:
# Set up logging.
# We'll use a second FileWriter to summarize accuracy on
# the test set. This will let us display it nicely in TensorBoard.
train_writer = tf.summary.FileWriter(os.path.join(LOGDIR, "train"))
train_writer.add_graph(sess.graph)
test_writer = tf.summary.FileWriter(os.path.join(LOGDIR, "test"))
summary_op = tf.summary.merge_all()
In [36]:
sess.run(tf.global_variables_initializer())
In [37]:
for step in range(TRAIN_STEPS):
batch_xs, batch_ys = mnist.train.next_batch(BATCH_SIZE)
summary_result, _ = sess.run([summary_op, train],
feed_dict={images: batch_xs, labels: batch_ys})
train_writer.add_summary(summary_result, step)
train_writer.add_run_metadata(tf.RunMetadata(), 'step%03d' % step)
# calculate accuracy on the test set, every 100 steps.
# we're using the entire test set here, so this will be a bit slow
if step % 100 == 0:
summary_result, acc = sess.run([summary_op, accuracy],
feed_dict={images: mnist.test.images,
labels: mnist.test.labels})
test_writer.add_summary(summary_result, step)
test_writer.add_run_metadata(tf.RunMetadata(), 'step%03d' % step)
print ("test accuracy: %f at step %d" % (acc, step))
print("Accuracy %f" % sess.run(accuracy,
feed_dict={images: mnist.test.images,
labels: mnist.test.labels}))
train_writer.close()
test_writer.close()
test accuracy: 0.195400 at step 0
test accuracy: 0.921200 at step 100
test accuracy: 0.935100 at step 200
test accuracy: 0.952700 at step 300
test accuracy: 0.954200 at step 400
test accuracy: 0.955100 at step 500
test accuracy: 0.966300 at step 600
test accuracy: 0.967200 at step 700
test accuracy: 0.969800 at step 800
test accuracy: 0.968000 at step 900
test accuracy: 0.969900 at step 1000
test accuracy: 0.971600 at step 1100
test accuracy: 0.968600 at step 1200
test accuracy: 0.974100 at step 1300
test accuracy: 0.970700 at step 1400
test accuracy: 0.973900 at step 1500
test accuracy: 0.975100 at step 1600
test accuracy: 0.976300 at step 1700
test accuracy: 0.975200 at step 1800
test accuracy: 0.977100 at step 1900
Accuracy 0.976900
In [38]:
x_dict = {'x': x_train}
train_input = tf.estimator.inputs.numpy_input_fn(x_dict, y_train,
shuffle=True,
num_epochs=None) # repeat forever
In [39]:
features = [tf.feature_column.numeric_column('x')]
After you run this next block, you should see an output line in the logs resembling:
WARNING:tensorflow:Using temporary folder as model directory: /var/folders/sf/j86k2fg96m96w2hmwlsdrvr8006h_5/T/tmpSkPFHV
You can then start TensorBoard, pointing to that directory like this:
$ tensorboard --logdir=/var/folders/sf/j86k2fg96m96w2hmwlsdrvr8006h_5/T/tmpSkPFHV
In [40]:
estimator = tf.estimator.LinearRegressor(features)
estimator.train(train_input, steps=1000)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-40-9f19127bb5bf> in <module>()
----> 1 estimator = tf.estimator.LinearRegressor(features)
2 estimator.train(train_input, steps=1000)
AttributeError: 'module' object has no attribute 'LinearRegressor'
In [41]:
data_source = tf.estimator.inputs.numpy_input_fn({'x': x_test}, shuffle=False)
predictions = list(estimator.predict(data_source))
preds = [p['predictions'][0] for p in predictions]
#for y in predictions:
# print(y['predictions'])
#predictions
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-41-5f5c49da3b8e> in <module>()
1 data_source = tf.estimator.inputs.numpy_input_fn({'x': x_test}, shuffle=False)
2
----> 3 predictions = list(estimator.predict(data_source))
4 preds = [p['predictions'][0] for p in predictions]
5
NameError: name 'estimator' is not defined
In [42]:
pylab.scatter(x_train, y_train)
pylab.plot(x_test, np.array(preds), 'g')
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-42-de08afcaecc1> in <module>()
1 pylab.scatter(x_train, y_train)
----> 2 pylab.plot(x_test, np.array(preds), 'g')
NameError: name 'preds' is not defined
Hopefully, this felt easier that the lower-level code above.
Here, we'll work with the "Adult dataset" from the U.S. Census bureau. Our task will be to predict whether an individual makes more than $50,000 a year based attributes such as education, hours of work per week, etc. More about this dataset is here.
Our goal here is to demonstrate how to work with data you might represent in a CSV file. Hopefully, you can adapt this code to a problem you care about.
The code presented here can be adapted to any CSV dataset that fits in memory (using the pandas input function) or a dataset of pretty much any size (using the Datasets API, below) - which contains logic to efficiently read it from disk. When you're training large models using GPUs, you want to be sure your input pipeline doesn't bottleneck (or starve) the GPU. The Datasets API handle this for you.
In [ ]:
census_train_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data'
census_train_path = tf.contrib.keras.utils.get_file('census.train', census_train_url)
census_test_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test'
census_test_path = tf.contrib.keras.utils.get_file('census.test', census_test_url)
In [ ]:
column_names = [
'age', 'workclass', 'fnlwgt', 'education', 'education-num',
'marital-status', 'occupation', 'relationship', 'race', 'sex',
'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',
'income'
]
census_train = pd.read_csv(census_train_path, index_col=False, names=column_names)
census_test = pd.read_csv(census_train_path, index_col=False, names=column_names)
census_train_label = census_train.pop('income') == " >50K"
census_test_label = census_test.pop('income') == " >50K"
In [ ]:
census_train.head(10)
In [ ]:
census_train_label[:20]
In [ ]:
train_input = tf.estimator.inputs.pandas_input_fn(
census_train,
census_train_label,
shuffle=True,
batch_size = 32, # process 32 examples at a time
num_epochs=None,
)
In [ ]:
test_input = tf.estimator.inputs.pandas_input_fn(
census_test,
census_test_label,
shuffle=True,
num_epochs=1)
In [ ]:
features, labels = train_input()
features
In [ ]:
features = [
tf.feature_column.numeric_column('hours-per-week'),
tf.feature_column.bucketized_column(tf.feature_column.numeric_column('education-num'), list(range(25))),
tf.feature_column.categorical_column_with_vocabulary_list('sex', ['male','female']),
tf.feature_column.categorical_column_with_hash_bucket('native-country', 1000),
]
In [ ]:
estimator = tf.estimator.LinearClassifier(features, model_dir='census/linear',n_classes=2)
In [ ]:
estimator.train(train_input, steps=5000)
In [ ]:
estimator.evaluate(test_input)
In [ ]:
predictions = [p for p in estimator.predict(test_input)]
print (predictions[0]["probabilities"])
In [ ]:
features = [
tf.feature_column.numeric_column('education-num'),
tf.feature_column.numeric_column('hours-per-week'),
tf.feature_column.numeric_column('age'),
tf.feature_column.indicator_column(
tf.feature_column.categorical_column_with_vocabulary_list('sex',['male','female'])),
tf.feature_column.embedding_column( # now using embedding!
tf.feature_column.categorical_column_with_hash_bucket('native-country', 1000), 10)
]
In [ ]:
estimator = tf.estimator.DNNClassifier(hidden_units=[20,20],
feature_columns=features,
n_classes=2,
model_dir='census/dnn')
In [ ]:
estimator.train(train_input, steps=5000)
In [ ]:
estimator.evaluate(test_input)
In [ ]:
def census_input_fn(path):
def input_fn():
dataset = (
tf.contrib.data.TextLineDataset(path)
.map(csv_decoder)
.shuffle(buffer_size=100)
.batch(32)
.repeat())
columns = dataset.make_one_shot_iterator().get_next()
income = tf.equal(columns.pop('income')," >50K")
return columns, income
return input_fn
In [ ]:
csv_defaults = collections.OrderedDict([
('age',[0]),
('workclass',['']),
('fnlwgt',[0]),
('education',['']),
('education-num',[0]),
('marital-status',['']),
('occupation',['']),
('relationship',['']),
('race',['']),
('sex',['']),
('capital-gain',[0]),
('capital-loss',[0]),
('hours-per-week',[0]),
('native-country',['']),
('income',['']),
])
In [ ]:
def csv_decoder(line):
parsed = tf.decode_csv(line, csv_defaults.values())
return dict(zip(csv_defaults.keys(), parsed))
In [ ]:
tf.reset_default_graph()
census_input = census_input_fn(census_train_path)
training_batch = census_input()
In [ ]:
with tf.Session() as sess:
features, high_income = sess.run(training_batch)
In [ ]:
print(features['education'])
In [ ]:
print(features['age'])
In [ ]:
print(high_income)
In [43]:
train,test = tf.contrib.keras.datasets.mnist.load_data()
x_train,y_train = train
x_test,y_test = test
mnist_train_input = tf.estimator.inputs.numpy_input_fn({'x':np.array(x_train, dtype=np.float32)},
np.array(y_train,dtype=np.int32),
shuffle=True,
num_epochs=None)
mnist_test_input = tf.estimator.inputs.numpy_input_fn({'x':np.array(x_test, dtype=np.float32)},
np.array(y_test,dtype=np.int32),
shuffle=True,
num_epochs=1)
In [44]:
estimator = tf.estimator.LinearClassifier([tf.feature_column.numeric_column('x',shape=784)],
n_classes=10,
model_dir="mnist/linear")
estimator.train(mnist_train_input, steps = 10000)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-44-87247606f207> in <module>()
----> 1 estimator = tf.estimator.LinearClassifier([tf.feature_column.numeric_column('x',shape=784)],
2 n_classes=10,
3 model_dir="mnist/linear")
4 estimator.train(mnist_train_input, steps = 10000)
AttributeError: 'module' object has no attribute 'LinearClassifier'
In [45]:
estimator.evaluate(mnist_test_input)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-45-b8dd5b7b1889> in <module>()
----> 1 estimator.evaluate(mnist_test_input)
NameError: name 'estimator' is not defined
$> tensorboard --logdir mnnist/DNN
In [46]:
estimator = tf.estimator.DNNClassifier(hidden_units=[256],
feature_columns=[tf.feature_column.numeric_column('x',shape=784)],
n_classes=10,
model_dir="mnist/DNN")
estimator.train(mnist_train_input, steps = 10000)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-46-98518774b0cc> in <module>()
----> 1 estimator = tf.estimator.DNNClassifier(hidden_units=[256],
2 feature_columns=[tf.feature_column.numeric_column('x',shape=784)],
3 n_classes=10,
4 model_dir="mnist/DNN")
5 estimator.train(mnist_train_input, steps = 10000)
AttributeError: 'module' object has no attribute 'DNNClassifier'
In [47]:
estimator.evaluate(mnist_test_input)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-47-b8dd5b7b1889> in <module>()
----> 1 estimator.evaluate(mnist_test_input)
NameError: name 'estimator' is not defined
In [48]:
# Parameters
BATCH_SIZE = 128
STEPS = 10000
In [49]:
def build_cnn(input_layer, mode):
with tf.name_scope("conv1"):
conv1 = tf.layers.conv2d(inputs=input_layer,filters=32, kernel_size=[5, 5],
padding='same', activation=tf.nn.relu)
with tf.name_scope("pool1"):
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
with tf.name_scope("conv2"):
conv2 = tf.layers.conv2d(inputs=pool1,filters=64, kernel_size=[5, 5],
padding='same', activation=tf.nn.relu)
with tf.name_scope("pool2"):
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
with tf.name_scope("dense"):
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
with tf.name_scope("dropout"):
is_training_mode = mode == tf.estimator.ModeKeys.TRAIN
dropout = tf.layers.dropout(inputs=dense, rate=0.4, training=is_training_mode)
logits = tf.layers.dense(inputs=dropout, units=10)
return logits
In [53]:
def model_fn(features, labels, mode):
# Describing the model
input_layer = tf.reshape(features['x'], [-1, 28, 28, 1])
tf.summary.image('mnist_input',input_layer)
logits = build_cnn(input_layer, mode)
# Generate Predictions
classes = tf.argmax(input=logits, axis=1)
predictions = {
'classes': classes,
'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
}
if mode == tf.estimator.ModeKeys.PREDICT:
# Return an EstimatorSpec object
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
with tf.name_scope('loss'):
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits)
loss = tf.reduce_sum(loss)
tf.summary.scalar('loss', loss)
with tf.name_scope('accuracy'):
accuracy = tf.cast(tf.equal(tf.cast(classes,tf.int32),labels),tf.float32)
accuracy = tf.reduce_mean(accuracy)
tf.summary.scalar('accuracy', accuracy)
# Configure the Training Op (for TRAIN mode)
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = tf.contrib.layers.optimize_loss(
loss=loss,
global_step=tf.train.get_global_step(),
learning_rate=1e-4,
optimizer='Adam')
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions,
loss=loss, train_op=train_op)
# Configure the accuracy metric for evaluation
eval_metric_ops = {
'accuracy': tf.metrics.accuracy(
classes,
labels)
}
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions,
loss=loss, eval_metric_ops=eval_metric_ops)
In [57]:
# create estimator
run_config = tf.contrib.learn.RunConfig(model_dir='mnist/CNN')
estimator = tf.estimator.Estimator(model_fn=model_fn, config=run_config)
# train for 1000 steps
# this is too few
#estimator.train(input_fn=mnist_train_input, steps=100)
# evaluate
evaluation = estimator.evaluate(input_fn=mnist_test_input)
print(evaluation)
# predict
preds = estimator.predict(input_fn=mnist_test_input)
#for p in preds:
# print(p)
INFO:tensorflow:Using config: {'_keep_checkpoint_every_n_hours': 10000, '_save_summary_steps': 100, '_task_type': None, '_master': '', '_session_config': None, '_save_checkpoints_secs': 600, '_environment': 'local', '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f6c191a6f28>, '_keep_checkpoint_max': 5, '_tf_random_seed': None, '_task_id': 0, '_save_checkpoints_steps': None, '_num_worker_replicas': 0, '_model_dir': 'mnist/CNN', '_is_chief': True, '_evaluation_master': '', '_num_ps_replicas': 0, '_tf_config': gpu_options {
per_process_gpu_memory_fraction: 1.0
}
}
INFO:tensorflow:Starting evaluation at 2017-08-04-22:13:38
INFO:tensorflow:Restoring parameters from mnist/CNN/model.ckpt-101
INFO:tensorflow:Finished evaluation at 2017-08-04-22:13:43
INFO:tensorflow:Saving dict for global step 101: accuracy = 0.9362, global_step = 101, loss = 40.0468
{'accuracy': 0.93620002, 'loss': 40.046825, 'global_step': 101}
INFO:tensorflow:Restoring parameters from mnist/CNN/model.ckpt-101
{'classes': 7, 'probabilities': array([ 3.44572050e-15, 1.99315103e-14, 7.24314875e-09,
3.53269913e-11, 1.15786963e-11, 3.55903627e-14,
3.74263096e-17, 1.00000000e+00, 1.79576162e-13,
2.90719306e-08], dtype=float32)}
{'classes': 2, 'probabilities': array([ 7.17364644e-13, 1.83419035e-12, 9.99999881e-01,
1.10234781e-08, 1.13141078e-17, 1.02130522e-12,
1.64995626e-07, 5.52519426e-18, 1.28749355e-14,
6.47574224e-18], dtype=float32)}
{'classes': 6, 'probabilities': array([ 1.06117906e-08, 4.21066579e-05, 2.69368769e-07,
6.38617492e-10, 6.46344178e-09, 1.39442335e-07,
9.99957442e-01, 1.49742233e-11, 9.21167285e-08,
1.52622992e-09], dtype=float32)}
{'classes': 0, 'probabilities': array([ 1.00000000e+00, 2.84085480e-16, 4.85532404e-12,
7.39682854e-18, 3.20734718e-21, 4.05162492e-12,
9.75580207e-12, 4.00647477e-17, 1.24142103e-12,
1.57844021e-13], dtype=float32)}
{'classes': 3, 'probabilities': array([ 2.81326802e-11, 8.79013982e-08, 1.71087553e-08,
9.99989629e-01, 1.76892550e-12, 1.02405256e-05,
9.70602487e-10, 4.31638097e-14, 7.51908480e-10,
1.45036049e-11], dtype=float32)}
{'classes': 6, 'probabilities': array([ 1.00703596e-06, 6.66866388e-12, 4.43731329e-10,
5.80729448e-14, 8.90221397e-10, 2.15194043e-11,
9.99999046e-01, 2.37752699e-12, 3.23695701e-08,
2.66472124e-08], dtype=float32)}
{'classes': 8, 'probabilities': array([ 5.59069846e-10, 8.80020707e-07, 8.92246362e-07,
1.57405702e-05, 3.84249091e-11, 6.00780538e-07,
2.68102957e-10, 9.21706885e-07, 9.99980807e-01,
1.23862847e-07], dtype=float32)}
{'classes': 7, 'probabilities': array([ 5.20086543e-18, 1.83508729e-11, 1.95589411e-14,
4.07754169e-12, 7.40081909e-15, 5.65846155e-12,
5.37035607e-20, 1.00000000e+00, 3.75602067e-12,
6.83243817e-09], dtype=float32)}
{'classes': 3, 'probabilities': array([ 7.56361120e-14, 5.34261288e-15, 2.59508814e-09,
9.99998450e-01, 6.86109774e-15, 1.53660164e-06,
6.63735388e-16, 7.42065321e-15, 3.63005959e-09,
4.29633282e-12], dtype=float32)}
{'classes': 7, 'probabilities': array([ 1.33029015e-14, 3.11738704e-19, 4.64131786e-18,
2.96810909e-11, 2.17201186e-15, 4.65667869e-13,
2.25007809e-18, 1.00000000e+00, 7.66483873e-16,
1.61143612e-10], dtype=float32)}
{'classes': 4, 'probabilities': array([ 1.72036417e-13, 1.37037603e-12, 5.21160441e-11,
1.93512850e-09, 9.94376957e-01, 2.53912820e-08,
4.56626967e-12, 1.04013952e-05, 1.13411174e-04,
5.49919065e-03], dtype=float32)}
{'classes': 5, 'probabilities': array([ 2.40595459e-19, 4.71665171e-20, 6.59772491e-18,
1.63663357e-15, 2.61035276e-20, 1.00000000e+00,
1.95209484e-23, 8.98813626e-19, 1.38256758e-11,
9.86325057e-20], dtype=float32)}
{'classes': 5, 'probabilities': array([ 1.97522022e-05, 1.00320658e-08, 9.08148001e-11,
3.42296992e-14, 8.69670131e-08, 9.98339415e-01,
3.15821031e-04, 1.62733637e-13, 1.32489682e-03,
1.89182905e-11], dtype=float32)}
{'classes': 1, 'probabilities': array([ 6.09828757e-13, 9.99989271e-01, 5.11722682e-08,
6.02290839e-09, 4.58782239e-08, 2.85167729e-08,
2.43408316e-09, 1.07358022e-09, 1.06269617e-05,
1.07490745e-10], dtype=float32)}
{'classes': 6, 'probabilities': array([ 1.49478856e-12, 1.00393211e-13, 1.90693988e-12,
6.81149744e-14, 2.05044965e-12, 4.33901448e-08,
1.00000000e+00, 3.21102646e-17, 5.22962160e-11,
3.60342485e-12], dtype=float32)}
{'classes': 9, 'probabilities': array([ 6.13028703e-07, 1.55959923e-09, 5.15116769e-11,
2.58841328e-06, 8.88553313e-06, 3.77600344e-07,
3.38300256e-11, 1.26482410e-04, 1.37022199e-10,
9.99861002e-01], dtype=float32)}
{'classes': 4, 'probabilities': array([ 2.16688728e-04, 9.22539434e-10, 2.73581371e-02,
2.30925938e-11, 9.71907556e-01, 1.33196882e-12,
5.00642811e-04, 8.47872272e-10, 1.69681971e-05,
9.93521798e-09], dtype=float32)}
{'classes': 4, 'probabilities': array([ 1.31109018e-13, 1.02882666e-07, 2.91667281e-11,
1.91390921e-08, 9.98771608e-01, 9.46144166e-04,
1.12685434e-07, 2.25364231e-04, 5.54748149e-05,
1.22911808e-06], dtype=float32)}
{'classes': 0, 'probabilities': array([ 1.00000000e+00, 2.19874347e-18, 1.30010867e-14,
3.73309842e-21, 1.81435884e-18, 1.09842621e-10,
2.98959711e-11, 1.11801831e-21, 2.57892475e-11,
8.29174184e-17], dtype=float32)}
{'classes': 1, 'probabilities': array([ 2.29805273e-14, 9.99999881e-01, 2.67508526e-09,
2.19985544e-10, 8.35913561e-09, 6.27948253e-08,
3.76219576e-11, 4.49308091e-11, 5.24366017e-09,
9.28000343e-10], dtype=float32)}
{'classes': 7, 'probabilities': array([ 1.88746561e-13, 3.09290534e-16, 5.10322444e-11,
5.82058577e-12, 4.63950222e-13, 7.83556908e-14,
4.77990552e-17, 9.99999166e-01, 7.48346281e-07,
3.18696181e-08], dtype=float32)}
{'classes': 9, 'probabilities': array([ 2.03440997e-12, 1.18451950e-08, 2.82821835e-12,
9.48345615e-08, 5.15013235e-03, 3.61524286e-08,
9.32553514e-12, 5.21408620e-05, 1.37693428e-06,
9.94796216e-01], dtype=float32)}
{'classes': 1, 'probabilities': array([ 6.40599795e-11, 9.99999642e-01, 6.73641698e-10,
1.35295507e-12, 3.38547181e-08, 6.43214995e-11,
2.11897117e-07, 3.12185694e-10, 1.42780749e-07,
6.72004585e-09], dtype=float32)}
{'classes': 5, 'probabilities': array([ 5.66199105e-08, 6.98951476e-13, 2.31572660e-11,
2.62880971e-12, 2.56521981e-09, 9.99999762e-01,
7.06550790e-11, 4.47880233e-10, 3.67880162e-08,
2.29383474e-07], dtype=float32)}
{'classes': 8, 'probabilities': array([ 3.37409517e-07, 6.58982174e-11, 3.50934348e-09,
1.16518947e-10, 5.01268601e-12, 1.15553391e-07,
1.04432313e-11, 1.28935448e-16, 9.99999523e-01,
3.35601769e-09], dtype=float32)}
{'classes': 0, 'probabilities': array([ 9.91834402e-01, 4.36002892e-07, 3.43864892e-09,
7.32610147e-07, 4.52541499e-05, 2.24636897e-04,
6.74580457e-03, 3.82989441e-07, 1.14828767e-03,
1.82911808e-09], dtype=float32)}
{'classes': 2, 'probabilities': array([ 2.77122682e-11, 2.76151435e-10, 9.99991417e-01,
7.84114673e-06, 7.76631038e-13, 6.27112459e-11,
2.59849705e-13, 4.07569786e-07, 1.21412108e-11,
4.06556154e-07], dtype=float32)}
{'classes': 9, 'probabilities': array([ 2.45247122e-07, 8.35380897e-16, 6.35577906e-15,
1.07115895e-13, 3.86495174e-07, 1.34560335e-10,
4.58224623e-15, 4.45247714e-08, 1.06383817e-11,
9.99999285e-01], dtype=float32)}
{'classes': 0, 'probabilities': array([ 9.99246955e-01, 8.72506440e-13, 9.90213357e-06,
1.58715793e-07, 5.03570201e-14, 3.50151311e-07,
6.78632603e-07, 1.36758450e-14, 7.41979398e-04,
1.98923322e-09], dtype=float32)}
{'classes': 1, 'probabilities': array([ 7.08045886e-14, 9.99999881e-01, 5.72265256e-11,
4.08470674e-10, 5.07915345e-08, 2.46846238e-10,
2.11420867e-10, 4.17654140e-11, 7.70360629e-08,
2.23318697e-09], dtype=float32)}
{'classes': 7, 'probabilities': array([ 3.81430532e-09, 8.48848010e-13, 2.20760584e-01,
4.39111027e-05, 2.50233310e-08, 1.69681975e-08,
2.27112513e-11, 7.79195189e-01, 2.20269484e-07,
2.35922748e-08], dtype=float32)}
{'classes': 8, 'probabilities': array([ 4.37283855e-11, 1.07111568e-11, 5.46662227e-09,
1.24781210e-11, 5.03944109e-13, 1.71735355e-08,
3.75557942e-14, 8.67716652e-13, 9.99999881e-01,
1.43311183e-07], dtype=float32)}
{'classes': 6, 'probabilities': array([ 1.74591244e-12, 6.96427182e-13, 3.42546731e-14,
1.23185515e-18, 2.33899434e-13, 1.65637361e-12,
1.00000000e+00, 3.26366944e-18, 6.20007043e-14,
9.03245548e-16], dtype=float32)}
{'classes': 3, 'probabilities': array([ 3.74674812e-07, 1.36410283e-09, 2.77776890e-09,
9.88712072e-01, 7.38311148e-12, 1.14319019e-03,
1.96963654e-08, 8.35808805e-07, 1.01430668e-02,
4.87216653e-07], dtype=float32)}
{'classes': 8, 'probabilities': array([ 2.00228234e-09, 1.30552935e-09, 2.22087521e-02,
2.82883803e-07, 1.83169452e-12, 9.17523280e-10,
3.37488856e-11, 1.49700530e-09, 9.77784038e-01,
6.89392937e-06], dtype=float32)}
{'classes': 9, 'probabilities': array([ 4.33621576e-11, 6.09984216e-11, 1.43784706e-11,
1.25040629e-06, 2.43390750e-05, 1.47952633e-05,
7.14300537e-12, 5.56177609e-02, 5.57544490e-06,
9.44336295e-01], dtype=float32)}
{'classes': 4, 'probabilities': array([ 8.78539672e-11, 6.65066837e-05, 4.30532367e-07,
2.39774323e-07, 9.50866282e-01, 4.86363133e-05,
1.24080538e-10, 6.66094820e-06, 4.08442102e-05,
4.89702784e-02], dtype=float32)}
{'classes': 7, 'probabilities': array([ 5.76714672e-24, 6.26044760e-22, 4.94524410e-16,
1.96372975e-17, 1.74274441e-24, 1.03880044e-23,
3.61587088e-28, 1.00000000e+00, 1.04958973e-18,
7.52323497e-18], dtype=float32)}
{'classes': 2, 'probabilities': array([ 4.06425699e-10, 4.63740797e-08, 9.99701560e-01,
1.52674773e-09, 4.32182380e-11, 4.09581286e-10,
6.12281568e-08, 2.78998417e-12, 2.98250583e-04,
1.11930918e-08], dtype=float32)}
{'classes': 9, 'probabilities': array([ 1.42849972e-14, 8.03136206e-16, 9.48364854e-14,
1.07320208e-17, 2.72657957e-07, 1.86990655e-15,
1.20462961e-15, 1.09508463e-07, 4.39329833e-13,
9.99999642e-01], dtype=float32)}
{'classes': 9, 'probabilities': array([ 5.07598230e-09, 6.65252367e-11, 1.40920049e-08,
1.14042907e-11, 1.25401689e-08, 2.43361026e-10,
6.03232256e-11, 2.53159360e-09, 4.26865370e-07,
9.99999523e-01], dtype=float32)}
{'classes': 9, 'probabilities': array([ 2.30472265e-16, 3.53491611e-11, 8.95284349e-14,
3.40881772e-12, 4.87049620e-05, 4.57463933e-09,
2.68512968e-13, 9.88044917e-07, 4.71936344e-11,
9.99950290e-01], dtype=float32)}
{'classes': 1, 'probabilities': array([ 1.82703347e-14, 9.99999523e-01, 2.08389817e-09,
1.56086213e-10, 3.09765568e-07, 6.83323336e-08,
2.78342377e-10, 5.85660853e-10, 3.01688949e-08,
8.04373720e-11], dtype=float32)}
{'classes': 1, 'probabilities': array([ 3.60828473e-13, 9.99999762e-01, 9.85808057e-08,
3.28820837e-09, 8.42515724e-10, 1.85817420e-11,
5.20492894e-12, 4.35240644e-09, 1.19559871e-07,
5.35994671e-09], dtype=float32)}
{'classes': 5, 'probabilities': array([ 7.81640291e-12, 5.05589291e-14, 1.15700201e-11,
1.27915799e-12, 4.85518692e-15, 1.00000000e+00,
2.36940551e-10, 6.15889886e-22, 2.79463896e-10,
1.36407795e-14], dtype=float32)}
{'classes': 0, 'probabilities': array([ 9.32942569e-01, 3.76717813e-09, 2.04475327e-06,
8.93920875e-12, 1.47287293e-12, 1.38074188e-02,
5.30536659e-02, 5.12740891e-11, 1.93648753e-04,
6.67765846e-07], dtype=float32)}
{'classes': 4, 'probabilities': array([ 3.33307817e-13, 1.22047676e-11, 8.60087446e-13,
7.34685614e-14, 9.99988317e-01, 3.33490076e-11,
1.02046302e-10, 8.74673611e-10, 3.90996749e-11,
1.17375475e-05], dtype=float32)}
{'classes': 4, 'probabilities': array([ 1.59801846e-13, 7.82010047e-12, 3.08650397e-11,
1.06776445e-11, 9.99994278e-01, 1.75945736e-09,
1.45463230e-09, 2.31406248e-11, 1.28798643e-08,
5.65912114e-06], dtype=float32)}
{'classes': 6, 'probabilities': array([ 4.01771074e-04, 3.41571237e-12, 7.06436853e-11,
2.55205385e-10, 4.12388040e-11, 1.09271072e-08,
9.99598205e-01, 1.60177524e-10, 6.75768641e-09,
2.57514149e-11], dtype=float32)}
{'classes': 2, 'probabilities': array([ 1.61705819e-17, 3.66294885e-12, 9.99999642e-01,
3.93744074e-07, 1.38120137e-19, 1.06543717e-14,
1.33272069e-13, 2.12319107e-18, 2.43152482e-12,
6.68858890e-20], dtype=float32)}
{'classes': 6, 'probabilities': array([ 1.99863294e-04, 2.44235456e-01, 2.62165213e-06,
1.10611076e-07, 2.92852693e-08, 5.67667093e-03,
7.49134481e-01, 2.21879262e-08, 7.50756823e-04,
1.19885146e-09], dtype=float32)}
{'classes': 3, 'probabilities': array([ 5.75623360e-12, 1.40710369e-22, 4.41010258e-07,
9.99999523e-01, 1.94040313e-13, 3.29060060e-15,
2.60517591e-22, 4.36759563e-14, 6.63898277e-12,
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{'classes': 0, 'probabilities': array([ 1.00000000e+00, 3.27629154e-19, 2.05019552e-12,
7.20938223e-16, 5.47914512e-18, 2.60780546e-12,
1.37652541e-12, 2.94182187e-15, 8.49331564e-13,
1.68670769e-13], dtype=float32)}
{'classes': 1, 'probabilities': array([ 7.00149709e-14, 9.99994636e-01, 4.77429296e-08,
1.39376466e-08, 2.45628939e-07, 1.12354155e-06,
1.92267979e-10, 3.38591155e-09, 4.00542149e-06,
6.59351773e-09], dtype=float32)}
{'classes': 5, 'probabilities': array([ 1.74281944e-03, 6.25630710e-12, 1.43533362e-12,
4.57242022e-05, 1.71974118e-16, 9.98211503e-01,
1.37980270e-11, 6.60136008e-13, 5.24291757e-08,
2.78101171e-11], dtype=float32)}
{'classes': 7, 'probabilities': array([ 2.12813153e-11, 6.51871557e-10, 1.80033177e-09,
3.04365089e-10, 3.03656517e-10, 1.88695903e-08,
4.63790476e-15, 9.94797945e-01, 7.54832530e-10,
5.20208571e-03], dtype=float32)}
{'classes': 6, 'probabilities': array([ 1.11615066e-06, 3.15853334e-14, 8.41666147e-12,
2.34462867e-13, 6.09273165e-09, 1.99644378e-14,
9.99998927e-01, 8.55829123e-12, 2.03911488e-11,
1.45701796e-14], dtype=float32)}
{'classes': 1, 'probabilities': array([ 3.77563818e-14, 9.99997616e-01, 1.18106813e-08,
3.09150359e-08, 8.82151738e-08, 3.21331073e-09,
5.86768620e-11, 9.49339007e-09, 1.90175513e-07,
2.05548076e-06], dtype=float32)}
{'classes': 7, 'probabilities': array([ 5.66475772e-15, 5.45564977e-14, 3.83371262e-15,
8.66685602e-14, 2.59011389e-15, 2.09243794e-07,
1.27663985e-16, 9.99999762e-01, 1.59219415e-10,
2.84591883e-09], dtype=float32)}
{'classes': 6, 'probabilities': array([ 1.43288764e-06, 2.16808432e-04, 1.76297112e-06,
1.90076552e-10, 4.05946885e-05, 5.57714600e-07,
9.99685645e-01, 1.07830600e-09, 2.87152598e-05,
2.44471648e-05], dtype=float32)}
{'classes': 6, 'probabilities': array([ 3.09150266e-07, 1.01209235e-15, 1.08612994e-10,
2.81505793e-14, 2.97523796e-15, 9.02609367e-13,
9.99999642e-01, 1.28240894e-16, 3.87298293e-14,
1.31126840e-13], dtype=float32)}
{'classes': 8, 'probabilities': array([ 3.70185869e-03, 3.23555605e-09, 2.70076930e-13,
5.44909534e-11, 6.23591984e-18, 1.56309102e-02,
5.99514882e-11, 1.90293896e-12, 9.80667233e-01,
2.05094788e-10], dtype=float32)}
{'classes': 0, 'probabilities': array([ 9.35408115e-01, 3.20181837e-12, 2.04949030e-10,
4.29581953e-10, 2.97087910e-11, 1.30360904e-08,
6.45919219e-02, 1.75000760e-11, 6.15298160e-11,
1.19605790e-12], dtype=float32)}
{'classes': 0, 'probabilities': array([ 1.00000000e+00, 1.38864955e-21, 5.29604670e-17,
1.12281868e-20, 2.44774032e-21, 4.07813505e-11,
9.03990966e-14, 2.32748640e-20, 3.35815089e-13,
9.95147081e-18], dtype=float32)}
{'classes': 8, 'probabilities': array([ 4.75463202e-12, 1.38365838e-12, 7.67268139e-05,
1.15020743e-10, 5.00604593e-12, 2.42490072e-09,
3.48745487e-14, 9.72468356e-11, 9.99923229e-01,
1.75116602e-10], dtype=float32)}
{'classes': 0, 'probabilities': array([ 9.99994993e-01, 2.31571079e-15, 9.95439261e-16,
1.13656593e-17, 2.45564513e-17, 4.94722553e-06,
8.38388129e-15, 2.01733676e-13, 1.42919027e-11,
1.94921339e-15], dtype=float32)}
{'classes': 5, 'probabilities': array([ 5.77215740e-11, 4.96463706e-08, 3.08166036e-12,
4.19872687e-12, 1.60661345e-15, 9.99957800e-01,
1.57093470e-16, 2.55201845e-17, 4.22068224e-05,
3.61388409e-13], dtype=float32)}
{'classes': 5, 'probabilities': array([ 5.80034853e-10, 1.55505120e-09, 1.11118276e-11,
3.15418563e-13, 3.42435851e-13, 1.00000000e+00,
2.89896391e-16, 8.58482313e-11, 3.42607898e-08,
4.66903904e-10], dtype=float32)}
{'classes': 9, 'probabilities': array([ 1.69515107e-12, 2.34347514e-10, 5.06971039e-13,
5.18529004e-11, 5.21789014e-04, 2.69347267e-10,
3.03287872e-14, 2.03913933e-05, 9.95868166e-10,
9.99457777e-01], dtype=float32)}
{'classes': 9, 'probabilities': array([ 3.75552454e-13, 1.74785895e-15, 5.18999550e-14,
1.31845381e-13, 1.95094740e-09, 1.26872690e-08,
3.56154537e-16, 1.95704475e-09, 4.77002224e-08,
1.00000000e+00], dtype=float32)}
{'classes': 2, 'probabilities': array([ 5.18811123e-12, 1.90056802e-04, 9.99805629e-01,
2.55283823e-07, 2.34600975e-07, 1.32039379e-10,
3.62307787e-06, 1.69606063e-09, 2.23819598e-07,
6.72884359e-10], dtype=float32)}
{'classes': 3, 'probabilities': array([ 1.41579110e-10, 1.01991315e-11, 5.56704833e-07,
9.99999166e-01, 9.03827248e-16, 1.85042083e-07,
4.44323506e-13, 7.95077632e-12, 2.87646262e-08,
1.95534604e-12], dtype=float32)}
{'classes': 0, 'probabilities': array([ 9.99966741e-01, 4.81600385e-11, 3.88143100e-08,
9.43650540e-12, 6.34222275e-09, 1.87420866e-07,
3.29271934e-05, 4.67689429e-11, 5.56369102e-08,
3.48996942e-10], dtype=float32)}
{'classes': 3, 'probabilities': array([ 1.00037543e-08, 2.70896413e-12, 2.92227574e-04,
9.99702394e-01, 4.24834192e-16, 5.28843680e-09,
5.37582036e-06, 1.22428825e-12, 2.72448890e-08,
5.79122728e-14], dtype=float32)}
{'classes': 4, 'probabilities': array([ 2.02810728e-12, 4.65450469e-08, 7.03634040e-10,
3.79208505e-11, 9.99747932e-01, 1.72386745e-08,
1.04622380e-10, 9.01162457e-06, 6.54886617e-06,
2.36466469e-04], dtype=float32)}
{'classes': 2, 'probabilities': array([ 1.59923950e-15, 1.50009685e-10, 1.00000000e+00,
2.96297760e-08, 2.59466754e-12, 3.83687185e-16,
5.49388091e-10, 1.80289694e-09, 3.05704559e-11,
1.50078422e-13], dtype=float32)}
{'classes': 3, 'probabilities': array([ 5.27231201e-08, 6.65696280e-06, 4.81036637e-04,
9.63274240e-01, 1.96475169e-09, 3.52996285e-03,
6.85145343e-11, 4.66539272e-11, 3.26989070e-02,
9.20184812e-06], dtype=float32)}
{'classes': 7, 'probabilities': array([ 1.02246194e-13, 3.73691789e-12, 5.88824074e-13,
4.59469919e-12, 9.59405081e-13, 6.03490868e-12,
1.72019372e-17, 9.99999404e-01, 5.43560995e-11,
6.32972274e-07], dtype=float32)}
{'classes': 6, 'probabilities': array([ 1.15422313e-06, 5.07401683e-06, 4.69412807e-11,
1.09312667e-12, 5.81789061e-09, 5.19656605e-05,
9.99896884e-01, 7.00914311e-16, 4.47976709e-05,
1.52481922e-13], dtype=float32)}
{'classes': 0, 'probabilities': array([ 1.00000000e+00, 1.88492492e-23, 1.97090231e-19,
9.14617160e-18, 1.15610216e-22, 2.50069188e-10,
3.26635659e-20, 3.00657753e-17, 2.35802605e-14,
5.36471295e-17], dtype=float32)}
{'classes': 0, 'probabilities': array([ 1.00000000e+00, 1.18377352e-18, 9.67973751e-15,
4.91544758e-18, 4.34417422e-19, 1.25703235e-12,
6.06247908e-14, 3.18429877e-18, 9.61651210e-14,
7.62034991e-16], dtype=float32)}
{'classes': 1, 'probabilities': array([ 1.26160797e-14, 9.99999046e-01, 5.09159825e-10,
3.90434796e-10, 1.17286425e-09, 8.01067035e-09,
1.31338898e-11, 4.34209007e-08, 9.69075927e-07,
2.26583474e-10], dtype=float32)}
{'classes': 1, 'probabilities': array([ 2.17254244e-14, 9.99999642e-01, 1.30098154e-10,
3.92318400e-10, 1.19041266e-09, 1.10955760e-08,
2.68041145e-09, 6.79236615e-12, 3.79652562e-07,
1.32793412e-11], dtype=float32)}
{'classes': 2, 'probabilities': array([ 1.08648486e-10, 3.46628585e-12, 9.99990582e-01,
3.17071525e-08, 9.73981312e-12, 2.24250804e-14,
9.75518386e-13, 3.68374914e-10, 9.35893786e-06,
1.30683719e-09], dtype=float32)}
{'classes': 8, 'probabilities': array([ 8.27727035e-07, 3.92980512e-16, 1.51050972e-09,
8.94823115e-13, 9.06696889e-16, 1.57346875e-07,
2.44017508e-15, 3.99583037e-18, 9.99999046e-01,
2.20659997e-16], dtype=float32)}
{'classes': 2, 'probabilities': array([ 5.51264611e-06, 5.90641036e-10, 9.98462319e-01,
1.46092055e-03, 9.42099803e-08, 3.61891068e-08,
7.84898546e-09, 9.36062200e-11, 7.11015236e-05,
8.55603659e-08], dtype=float32)}
{'classes': 2, 'probabilities': array([ 2.01549701e-13, 1.87325502e-12, 1.00000000e+00,
5.09360909e-08, 8.14375650e-12, 5.20981424e-13,
6.26589058e-12, 4.42111354e-14, 4.12297930e-12,
1.47490378e-13], dtype=float32)}
{'classes': 2, 'probabilities': array([ 4.57301433e-14, 5.54021273e-10, 1.00000000e+00,
1.04629000e-10, 6.86838106e-17, 4.81535298e-12,
1.17422794e-10, 4.76807982e-10, 4.71526082e-11,
1.20569971e-10], dtype=float32)}
{'classes': 8, 'probabilities': array([ 1.55503541e-01, 5.66196867e-09, 3.83607755e-07,
6.89157168e-05, 8.94783553e-12, 2.14211084e-03,
3.54730990e-04, 1.09044327e-11, 8.41930330e-01,
8.97137642e-10], dtype=float32)}
{'classes': 5, 'probabilities': array([ 7.20947142e-08, 2.44853847e-15, 1.21874065e-14,
3.52518795e-12, 6.50913056e-15, 9.83815730e-01,
1.45712876e-14, 4.58922885e-13, 1.61841121e-02,
2.66000555e-10], dtype=float32)}
{'classes': 7, 'probabilities': array([ 5.55920778e-05, 7.51843816e-03, 1.21649238e-04,
1.01160542e-04, 1.13671922e-04, 9.25895665e-03,
9.28488042e-09, 8.25073004e-01, 5.27874931e-07,
1.57756910e-01], dtype=float32)}
{'classes': 6, 'probabilities': array([ 4.15350732e-09, 1.69405689e-15, 1.49860895e-07,
7.58785680e-17, 2.31598391e-14, 1.03521280e-11,
9.99999881e-01, 2.24342923e-16, 4.81405551e-13,
1.87581442e-16], dtype=float32)}
{'classes': 0, 'probabilities': array([ 1.00000000e+00, 1.36289969e-18, 1.52891995e-13,
1.67408824e-18, 6.03571697e-16, 6.65019290e-11,
3.22440165e-12, 7.46075967e-18, 4.93951546e-11,
1.08802737e-14], dtype=float32)}
{'classes': 7, 'probabilities': array([ 6.24665411e-16, 1.64958487e-13, 1.46561300e-17,
1.02848546e-12, 3.79135932e-13, 5.72502545e-10,
1.03116738e-18, 1.00000000e+00, 3.09151467e-13,
1.26988411e-08], dtype=float32)}
{'classes': 7, 'probabilities': array([ 2.97007952e-15, 2.06544986e-14, 3.05436851e-16,
1.55477756e-10, 2.50942591e-14, 9.18841836e-10,
4.47057107e-16, 1.00000000e+00, 1.95884003e-10,
1.10464782e-09], dtype=float32)}
{'classes': 7, 'probabilities': array([ 1.96191050e-11, 9.22663261e-08, 1.18626200e-01,
4.57791379e-03, 1.27019975e-02, 3.98595660e-07,
5.28655564e-09, 8.64092588e-01, 8.41851261e-07,
4.28614300e-09], dtype=float32)}
{'classes': 3, 'probabilities': array([ 6.65249467e-10, 6.19166177e-13, 2.38826862e-13,
9.99664783e-01, 4.45210734e-16, 3.35220364e-04,
4.53164763e-12, 2.39063750e-14, 1.72507271e-08,
1.06036057e-12], dtype=float32)}
{'classes': 7, 'probabilities': array([ 3.74890206e-18, 7.34872749e-20, 6.13366627e-16,
7.17596254e-16, 4.99834018e-19, 1.14753708e-17,
1.75654021e-24, 1.00000000e+00, 6.99605076e-17,
2.49072376e-14], dtype=float32)}
{'classes': 3, 'probabilities': array([ 1.15681860e-10, 4.12554491e-10, 4.61237226e-09,
9.99976397e-01, 7.52192530e-10, 9.45536340e-06,
8.68070373e-13, 8.76386196e-07, 1.55237558e-07,
1.32417535e-05], dtype=float32)}
{'classes': 6, 'probabilities': array([ 1.31465914e-08, 2.53265739e-07, 4.25317751e-07,
2.82363768e-11, 1.19248222e-09, 1.07685533e-04,
9.99861717e-01, 7.59525038e-13, 2.97512652e-05,
9.93748994e-09], dtype=float32)}
{'classes': 9, 'probabilities': array([ 3.93990035e-10, 1.47224423e-14, 1.52599357e-11,
8.98848528e-14, 4.60434135e-09, 1.73544751e-12,
8.23482213e-15, 3.34944850e-11, 1.62863928e-12,
1.00000000e+00], dtype=float32)}
{'classes': 5, 'probabilities': array([ 1.89205166e-02, 7.96598613e-07, 4.32461384e-05,
7.35778605e-10, 1.89577679e-06, 9.65828836e-01,
4.16749645e-05, 6.23037224e-04, 7.94945008e-05,
1.44605916e-02], dtype=float32)}
{'classes': 4, 'probabilities': array([ 3.11197539e-07, 1.30605859e-10, 6.51087170e-12,
3.38464478e-12, 9.99992013e-01, 2.52177834e-08,
1.09510285e-08, 1.00654806e-11, 9.68503215e-08,
7.50931440e-06], dtype=float32)}
{'classes': 4, 'probabilities': array([ 1.11223155e-16, 3.60318193e-13, 9.30643027e-14,
3.08252018e-15, 9.99999881e-01, 2.50064639e-12,
1.43618231e-14, 5.20998078e-10, 1.80562735e-11,
1.57147113e-07], dtype=float32)}
{'classes': 8, 'probabilities': array([ 3.78853385e-03, 1.35289300e-08, 1.08642314e-06,
1.10891363e-09, 2.59844928e-05, 5.11019134e-05,
1.65819552e-06, 6.50816044e-13, 9.95994449e-01,
1.37129449e-04], dtype=float32)}
{'classes': 5, 'probabilities': array([ 6.38806386e-09, 5.38947630e-13, 1.95852381e-14,
8.67784407e-15, 6.25873458e-14, 9.99728143e-01,
8.99948766e-18, 4.13130537e-11, 2.71842116e-04,
1.11989362e-09], dtype=float32)}
{'classes': 0, 'probabilities': array([ 9.99999881e-01, 8.35710150e-15, 2.85881561e-11,
8.32615972e-14, 4.79266540e-12, 4.92979746e-09,
8.07030354e-09, 3.76314452e-10, 1.30842635e-08,
6.28228563e-08], dtype=float32)}
{'classes': 1, 'probabilities': array([ 6.73491957e-11, 9.99997139e-01, 2.49658272e-09,
2.94512775e-10, 5.13229814e-09, 1.95161931e-09,
5.07007982e-08, 1.45038244e-11, 2.90212370e-06,
1.84474061e-10], dtype=float32)}
{'classes': 9, 'probabilities': array([ 6.59904043e-13, 1.86289455e-08, 1.07305587e-09,
1.59519370e-12, 8.54492304e-04, 1.50530011e-09,
5.56231493e-13, 7.84526719e-07, 3.73620367e-07,
9.99144316e-01], dtype=float32)}
{'classes': 1, 'probabilities': array([ 9.72513847e-10, 9.99998093e-01, 7.22392215e-11,
3.34915927e-12, 9.10837961e-10, 6.95130975e-12,
1.78929588e-06, 1.61700278e-10, 1.45260714e-07,
8.58947913e-10], dtype=float32)}
{'classes': 0, 'probabilities': array([ 1.00000000e+00, 1.85951194e-18, 1.75476699e-11,
4.21915480e-14, 3.55543320e-18, 1.19351458e-12,
3.64200900e-12, 5.41665976e-16, 1.04961080e-16,
9.62633966e-12], dtype=float32)}
{'classes': 6, 'probabilities': array([ 1.85579538e-05, 5.14288316e-04, 8.67833383e-04,
6.37008052e-04, 7.78710898e-07, 3.25438737e-06,
8.81668448e-01, 1.16246350e-01, 4.31721564e-05,
3.59163579e-07], dtype=float32)}
{'classes': 4, 'probabilities': array([ 1.79492787e-09, 5.89185312e-10, 5.55958361e-12,
3.64041967e-13, 9.99998927e-01, 1.47132075e-08,
3.12225126e-08, 5.98662631e-09, 1.42967982e-09,
1.09654331e-06], dtype=float32)}
{'classes': 6, 'probabilities': array([ 5.66686129e-13, 5.18945831e-15, 5.36235336e-12,
8.14381733e-16, 2.63738337e-12, 1.68564260e-10,
1.00000000e+00, 9.00540879e-17, 9.00576610e-14,
8.73099011e-13], dtype=float32)}
{'classes': 0, 'probabilities': array([ 9.99992847e-01, 1.72549041e-12, 2.24857510e-09,
1.22917518e-11, 4.03757527e-10, 8.29525220e-07,
5.74752301e-10, 2.52738408e-09, 1.77347204e-08,
6.34084972e-06], dtype=float32)}
{'classes': 4, 'probabilities': array([ 3.79471921e-09, 2.79196138e-06, 3.29946488e-08,
3.48234802e-10, 9.85949695e-01, 2.53325450e-08,
5.16452837e-06, 1.71251086e-04, 2.77515113e-07,
1.38707170e-02], dtype=float32)}
{'classes': 8, 'probabilities': array([ 2.56314678e-07, 4.95501270e-04, 2.02369144e-08,
6.61910988e-08, 5.65565621e-08, 5.41922311e-08,
1.81421378e-10, 1.80973270e-12, 9.99504089e-01,
8.67506600e-09], dtype=float32)}
{'classes': 3, 'probabilities': array([ 3.38242054e-02, 1.36716494e-06, 2.59297871e-04,
9.57661808e-01, 4.31098740e-10, 8.07188358e-03,
1.77411071e-04, 4.09878703e-06, 6.51018198e-11,
7.31170069e-10], dtype=float32)}
{'classes': 0, 'probabilities': array([ 9.99897957e-01, 1.67440931e-14, 5.15126461e-13,
3.41894632e-16, 3.30483139e-16, 1.01885795e-04,
6.90706823e-12, 1.51420035e-15, 6.00977401e-08,
1.43466586e-12], dtype=float32)}
{'classes': 7, 'probabilities': array([ 1.53382833e-15, 3.50088114e-09, 1.33926533e-05,
1.13749206e-08, 2.00792294e-09, 1.34078322e-13,
4.87948508e-14, 9.99986649e-01, 1.61240621e-08,
3.92638005e-11], dtype=float32)}
{'classes': 9, 'probabilities': array([ 4.14556323e-12, 2.23268005e-13, 2.20248698e-11,
8.41927569e-11, 1.84684961e-08, 1.32743008e-08,
1.08994658e-14, 5.14574161e-09, 2.05191003e-11,
1.00000000e+00], dtype=float32)}
In [ ]:
# Run an experiment
from tensorflow.contrib.learn.python.learn import learn_runner
# Enable TensorFlow logs
tf.logging.set_verbosity(tf.logging.INFO)
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# create experiment
def experiment_fn(run_config, hparams):
# create estimator
estimator = tf.estimator.Estimator(model_fn=model_fn,
config=run_config)
return tf.contrib.learn.Experiment(
estimator,
train_input_fn=train_input_fn,
eval_input_fn=test_input_fn,
train_steps=STEPS
)
# run experiment
learn_runner.run(experiment_fn,
run_config=run_config)
$> tensorboard --logdir mnist/CNN
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Content source: random-forests/tensorflow-workshop
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