Skip to content

Instantly share code, notes, and snippets.

@dragonforce2010

dragonforce2010/labels_1024.tsv

Forked from teamdandelion/labels_1024.tsv
Created March 11, 2017 22:24
Show Gist options
  • Save dragonforce2010/e6ece47d9d5cc57c77b4bb13c53e612a to your computer and use it in GitHub Desktop.
Save dragonforce2010/e6ece47d9d5cc57c77b4bb13c53e612a to your computer and use it in GitHub Desktop.
Download ZIP

Revisions

  1. Dandelion Mané revised this gist Feb 27, 2017. 3 changed files with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion mnist.py
    View file
    Original file line number Diff line number Diff line change
    @@ -17,7 +17,7 @@
    import urllib

    LOGDIR = '/tmp/mnist_tutorial/'
    GIST_URL = 'https://gist.github.com/dandelionmane/4f02ab8f1451e276fea1f165a20336f1/raw/a20c87f5e1f176e9abf677b46a74c6f2581c7bd8/'
    GIST_URL = 'https://gist.github.com/dandelionmane/4f02ab8f1451e276fea1f165a20336f1/raw/dfb8ee95b010480d56a73f324aca480b3820c180'

    ### MNIST EMBEDDINGS ###
    mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + 'data', one_hot=True)
    Binary file modified slides.pdf
    View file
    Binary file not shown.
    Binary file modified sprite_1024.png
    View file
    Loading
    Sorry, something went wrong. Reload?
    Sorry, we cannot display this file.
    Sorry, this file is invalid so it cannot be displayed.
  2. Daniel Mané revised this gist Feb 15, 2017. 1 changed file with 3 additions and 3 deletions.
    6 changes: 3 additions & 3 deletions mnist.py
    View file
    Original file line number Diff line number Diff line change
    @@ -49,7 +49,7 @@ def fc_layer(input, size_in, size_out, name="fc"):
    return act


    def mnist_model(learning_rate, use_two_conv, use_two_fc, LOGDIR):
    def mnist_model(learning_rate, use_two_conv, use_two_fc, hparam):
    tf.reset_default_graph()
    sess = tf.Session()

    @@ -101,7 +101,7 @@ def mnist_model(learning_rate, use_two_conv, use_two_fc, LOGDIR):
    saver = tf.train.Saver()

    sess.run(tf.global_variables_initializer())
    writer = tf.summary.FileWriter(LOGDIR)
    writer = tf.summary.FileWriter(LOGDIR + hparam)
    writer.add_graph(sess.graph)

    config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
    @@ -140,7 +140,7 @@ def main():
    print('Starting run for %s' % hparam)

    # Actually run with the new settings
    mnist_model(learning_rate, use_two_fc, use_two_conv, LOGDIR + hparam)
    mnist_model(learning_rate, use_two_fc, use_two_conv, hparam)


    if __name__ == '__main__':
  3. Daniel Mané revised this gist Feb 15, 2017. 1 changed file with 0 additions and 0 deletions.
    Binary file modified sprite_1024.png
    View file
    Loading
    Sorry, something went wrong. Reload?
    Sorry, we cannot display this file.
    Sorry, this file is invalid so it cannot be displayed.
  4. Daniel Mané revised this gist Feb 15, 2017. 1 changed file with 0 additions and 0 deletions.
    Binary file added slides.pdf
    View file
    Binary file not shown.
  5. Daniel Mané revised this gist Feb 15, 2017. 1 changed file with 5 additions and 5 deletions.
    10 changes: 5 additions & 5 deletions mnist.py
    View file
    Original file line number Diff line number Diff line change
    @@ -129,12 +129,12 @@ def make_hparam_string(learning_rate, use_two_fc, use_two_conv):
    return "lr_%.0E,%s,%s" % (learning_rate, conv_param, fc_param)

    def main():
    # You can try adding some more learning rates
    for learning_rate in [1E-3, 1E-4]:
    # You can try adding some more learning rates
    for learning_rate in [1E-4]:

    # You can also change to use fewer layers
    for use_two_fc in [True, False]:
    for use_two_conv in [True, False]:
    # Include "False" as a value to try different model architectures
    for use_two_fc in [True]:
    for use_two_conv in [True]:
    # Construct a hyperparameter string for each one (example: "lr_1E-3,fc=2,conv=2)
    hparam = make_hparam_string(learning_rate, use_two_fc, use_two_conv)
    print('Starting run for %s' % hparam)
  6. Daniel Mané revised this gist Feb 15, 2017. 1 changed file with 14 additions and 0 deletions.
    14 changes: 14 additions & 0 deletions mnist.py
    View file
    Original file line number Diff line number Diff line change
    @@ -1,3 +1,17 @@
    # Copyright 2017 Google, Inc. All Rights Reserved.
    #
    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    # http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    # ==============================================================================
    import os
    import tensorflow as tf
    import urllib
  7. Daniel Mané revised this gist Feb 15, 2017. 1 changed file with 15 additions and 12 deletions.
    27 changes: 15 additions & 12 deletions mnist.py
    View file
    Original file line number Diff line number Diff line change
    @@ -2,10 +2,14 @@
    import tensorflow as tf
    import urllib

    LOGDIR = '/tmp/mnist_tutorial/'
    GIST_URL = 'https://gist.github.com/dandelionmane/4f02ab8f1451e276fea1f165a20336f1/raw/a20c87f5e1f176e9abf677b46a74c6f2581c7bd8/'

    ### MNIST EMBEDDINGS ###
    mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir='/tmp/mnist_tutorial/data', one_hot=True)
    urllib.urlretrieve
    mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + 'data', one_hot=True)
    ### Get a sprite and labels file for the embedding projector ###
    urllib.urlretrieve(GIST_URL + 'labels_1024.tsv', LOGDIR + 'labels_1024.tsv')
    urllib.urlretrieve(GIST_URL + 'sprite_1024.png', LOGDIR + 'sprite_1024.png')


    def conv_layer(input, size_in, size_out, name="conv"):
    @@ -31,7 +35,7 @@ def fc_layer(input, size_in, size_out, name="fc"):
    return act


    def mnist_model(learning_rate, use_two_conv, use_two_fc, logdir):
    def mnist_model(learning_rate, use_two_conv, use_two_fc, LOGDIR):
    tf.reset_default_graph()
    sess = tf.Session()

    @@ -83,14 +87,14 @@ def mnist_model(learning_rate, use_two_conv, use_two_fc, logdir):
    saver = tf.train.Saver()

    sess.run(tf.global_variables_initializer())
    writer = tf.summary.FileWriter(logdir)
    writer = tf.summary.FileWriter(LOGDIR)
    writer.add_graph(sess.graph)

    config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
    embedding_config = config.embeddings.add()
    embedding_config.tensor_name = embedding.name
    embedding_config.sprite.image_path = '/tmp/mnist_tutorial/sprite_1024.png'
    embedding_config.metadata_path = '/tmp/mnist_tutorial/labels_1024.tsv'
    embedding_config.sprite.image_path = LOGDIR + 'sprite_1024.png'
    embedding_config.metadata_path = LOGDIR + 'labels_1024.tsv'
    # Specify the width and height of a single thumbnail.
    embedding_config.sprite.single_image_dim.extend([28, 28])
    tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config)
    @@ -102,7 +106,7 @@ def mnist_model(learning_rate, use_two_conv, use_two_fc, logdir):
    writer.add_summary(s, i)
    if i % 500 == 0:
    sess.run(assignment, feed_dict={x: mnist.test.images[:1024], y: mnist.test.labels[:1024]})
    saver.save(sess, os.path.join(logdir, "model.ckpt"), i)
    saver.save(sess, os.path.join(LOGDIR, "model.ckpt"), i)
    sess.run(train_step, feed_dict={x: batch[0], y: batch[1]})

    def make_hparam_string(learning_rate, use_two_fc, use_two_conv):
    @@ -112,18 +116,17 @@ def make_hparam_string(learning_rate, use_two_fc, use_two_conv):

    def main():
    # You can try adding some more learning rates
    for learning_rate in [1E-3]:
    for learning_rate in [1E-3, 1E-4]:

    # You can also change to use fewer layers
    for use_two_fc in [True]:
    for use_two_conv in [True]:
    for use_two_fc in [True, False]:
    for use_two_conv in [True, False]:
    # Construct a hyperparameter string for each one (example: "lr_1E-3,fc=2,conv=2)
    hparam = make_hparam_string(learning_rate, use_two_fc, use_two_conv)
    print('Starting run for %s' % hparam)

    logdir = "/tmp/mnist_tutorial/" + hparam
    # Actually run with the new settings
    mnist_model(learning_rate, use_two_fc, use_two_conv, logdir)
    mnist_model(learning_rate, use_two_fc, use_two_conv, LOGDIR + hparam)


    if __name__ == '__main__':
  8. Daniel Mané revised this gist Feb 15, 2017. 2 changed files with 1024 additions and 0 deletions.
    1,024 changes: 1,024 additions & 0 deletions labels_1024.tsv
    View file
    Original file line number Diff line number Diff line change
    @@ -0,0 +1,1024 @@
    7
    2
    1
    0
    4
    1
    4
    9
    5
    9
    0
    6
    9
    0
    1
    5
    9
    7
    3
    4
    9
    6
    6
    5
    4
    0
    7
    4
    0
    1
    3
    1
    3
    4
    7
    2
    7
    1
    2
    1
    1
    7
    4
    2
    3
    5
    1
    2
    4
    4
    6
    3
    5
    5
    6
    0
    4
    1
    9
    5
    7
    8
    9
    3
    7
    4
    6
    4
    3
    0
    7
    0
    2
    9
    1
    7
    3
    2
    9
    7
    7
    6
    2
    7
    8
    4
    7
    3
    6
    1
    3
    6
    9
    3
    1
    4
    1
    7
    6
    9
    6
    0
    5
    4
    9
    9
    2
    1
    9
    4
    8
    7
    3
    9
    7
    4
    4
    4
    9
    2
    5
    4
    7
    6
    7
    9
    0
    5
    8
    5
    6
    6
    5
    7
    8
    1
    0
    1
    6
    4
    6
    7
    3
    1
    7
    1
    8
    2
    0
    2
    9
    9
    5
    5
    1
    5
    6
    0
    3
    4
    4
    6
    5
    4
    6
    5
    4
    5
    1
    4
    4
    7
    2
    3
    2
    7
    1
    8
    1
    8
    1
    8
    5
    0
    8
    9
    2
    5
    0
    1
    1
    1
    0
    9
    0
    3
    1
    6
    4
    2
    3
    6
    1
    1
    1
    3
    9
    5
    2
    9
    4
    5
    9
    3
    9
    0
    3
    6
    5
    5
    7
    2
    2
    7
    1
    2
    8
    4
    1
    7
    3
    3
    8
    8
    7
    9
    2
    2
    4
    1
    5
    9
    8
    7
    2
    3
    0
    4
    4
    2
    4
    1
    9
    5
    7
    7
    2
    8
    2
    6
    8
    5
    7
    7
    9
    1
    8
    1
    8
    0
    3
    0
    1
    9
    9
    4
    1
    8
    2
    1
    2
    9
    7
    5
    9
    2
    6
    4
    1
    5
    8
    2
    9
    2
    0
    4
    0
    0
    2
    8
    4
    7
    1
    2
    4
    0
    2
    7
    4
    3
    3
    0
    0
    3
    1
    9
    6
    5
    2
    5
    9
    2
    9
    3
    0
    4
    2
    0
    7
    1
    1
    2
    1
    5
    3
    3
    9
    7
    8
    6
    5
    6
    1
    3
    8
    1
    0
    5
    1
    3
    1
    5
    5
    6
    1
    8
    5
    1
    7
    9
    4
    6
    2
    2
    5
    0
    6
    5
    6
    3
    7
    2
    0
    8
    8
    5
    4
    1
    1
    4
    0
    3
    3
    7
    6
    1
    6
    2
    1
    9
    2
    8
    6
    1
    9
    5
    2
    5
    4
    4
    2
    8
    3
    8
    2
    4
    5
    0
    3
    1
    7
    7
    5
    7
    9
    7
    1
    9
    2
    1
    4
    2
    9
    2
    0
    4
    9
    1
    4
    8
    1
    8
    4
    5
    9
    8
    8
    3
    7
    6
    0
    0
    3
    0
    2
    6
    6
    4
    9
    3
    3
    3
    2
    3
    9
    1
    2
    6
    8
    0
    5
    6
    6
    6
    3
    8
    8
    2
    7
    5
    8
    9
    6
    1
    8
    4
    1
    2
    5
    9
    1
    9
    7
    5
    4
    0
    8
    9
    9
    1
    0
    5
    2
    3
    7
    8
    9
    4
    0
    6
    3
    9
    5
    2
    1
    3
    1
    3
    6
    5
    7
    4
    2
    2
    6
    3
    2
    6
    5
    4
    8
    9
    7
    1
    3
    0
    3
    8
    3
    1
    9
    3
    4
    4
    6
    4
    2
    1
    8
    2
    5
    4
    8
    8
    4
    0
    0
    2
    3
    2
    7
    7
    0
    8
    7
    4
    4
    7
    9
    6
    9
    0
    9
    8
    0
    4
    6
    0
    6
    3
    5
    4
    8
    3
    3
    9
    3
    3
    3
    7
    8
    0
    8
    2
    1
    7
    0
    6
    5
    4
    3
    8
    0
    9
    6
    3
    8
    0
    9
    9
    6
    8
    6
    8
    5
    7
    8
    6
    0
    2
    4
    0
    2
    2
    3
    1
    9
    7
    5
    1
    0
    8
    4
    6
    2
    6
    7
    9
    3
    2
    9
    8
    2
    2
    9
    2
    7
    3
    5
    9
    1
    8
    0
    2
    0
    5
    2
    1
    3
    7
    6
    7
    1
    2
    5
    8
    0
    3
    7
    2
    4
    0
    9
    1
    8
    6
    7
    7
    4
    3
    4
    9
    1
    9
    5
    1
    7
    3
    9
    7
    6
    9
    1
    3
    7
    8
    3
    3
    6
    7
    2
    8
    5
    8
    5
    1
    1
    4
    4
    3
    1
    0
    7
    7
    0
    7
    9
    4
    4
    8
    5
    5
    4
    0
    8
    2
    1
    0
    8
    4
    5
    0
    4
    0
    6
    1
    7
    3
    2
    6
    7
    2
    6
    9
    3
    1
    4
    6
    2
    5
    4
    2
    0
    6
    2
    1
    7
    3
    4
    1
    0
    5
    4
    3
    1
    1
    7
    4
    9
    9
    4
    8
    4
    0
    2
    4
    5
    1
    1
    6
    4
    7
    1
    9
    4
    2
    4
    1
    5
    5
    3
    8
    3
    1
    4
    5
    6
    8
    9
    4
    1
    5
    3
    8
    0
    3
    2
    5
    1
    2
    8
    3
    4
    4
    0
    8
    8
    3
    3
    1
    7
    3
    5
    9
    6
    3
    2
    6
    1
    3
    6
    0
    7
    2
    1
    7
    1
    4
    2
    4
    2
    1
    7
    9
    6
    1
    1
    2
    4
    8
    1
    7
    7
    4
    8
    0
    7
    3
    1
    3
    1
    0
    7
    7
    0
    3
    5
    5
    2
    7
    6
    6
    9
    2
    8
    3
    5
    2
    2
    5
    6
    0
    8
    2
    9
    2
    8
    8
    8
    8
    7
    4
    9
    3
    0
    6
    6
    3
    2
    1
    3
    2
    2
    9
    3
    0
    0
    5
    7
    8
    1
    4
    4
    6
    0
    2
    9
    1
    4
    7
    4
    7
    3
    9
    8
    8
    4
    7
    1
    2
    1
    2
    2
    3
    2
    3
    2
    3
    9
    1
    7
    4
    0
    3
    5
    5
    8
    6
    3
    2
    6
    7
    6
    6
    3
    2
    7
    8
    1
    1
    7
    5
    6
    4
    9
    5
    1
    3
    3
    4
    7
    8
    9
    1
    1
    6
    9
    1
    4
    4
    5
    4
    0
    6
    2
    2
    3
    1
    5
    1
    2
    0
    3
    8
    1
    2
    6
    7
    1
    6
    2
    3
    9
    0
    1
    2
    2
    0
    8
    9
    9
    0
    2
    5
    1
    9
    7
    8
    1
    0
    4
    1
    7
    9
    6
    4
    2
    6
    8
    1
    3
    7
    5
    4
    Binary file added sprite_1024.png
    View file
    Loading
    Sorry, something went wrong. Reload?
    Sorry, we cannot display this file.
    Sorry, this file is invalid so it cannot be displayed.
  9. Dandelion Mané created this gist Feb 15, 2017.
    130 changes: 130 additions & 0 deletions mnist.py
    View file
    Original file line number Diff line number Diff line change
    @@ -0,0 +1,130 @@
    import os
    import tensorflow as tf
    import urllib

    ### MNIST EMBEDDINGS ###
    mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir='/tmp/mnist_tutorial/data', one_hot=True)
    urllib.urlretrieve
    ### Get a sprite and labels file for the embedding projector ###


    def conv_layer(input, size_in, size_out, name="conv"):
    with tf.name_scope(name):
    w = tf.Variable(tf.truncated_normal([5, 5, size_in, size_out], stddev=0.1), name="W")
    b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
    conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="SAME")
    act = tf.nn.relu(conv + b)
    tf.summary.histogram("weights", w)
    tf.summary.histogram("biases", b)
    tf.summary.histogram("activations", act)
    return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")


    def fc_layer(input, size_in, size_out, name="fc"):
    with tf.name_scope(name):
    w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name="W")
    b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
    act = tf.nn.relu(tf.matmul(input, w) + b)
    tf.summary.histogram("weights", w)
    tf.summary.histogram("biases", b)
    tf.summary.histogram("activations", act)
    return act


    def mnist_model(learning_rate, use_two_conv, use_two_fc, logdir):
    tf.reset_default_graph()
    sess = tf.Session()

    # Setup placeholders, and reshape the data
    x = tf.placeholder(tf.float32, shape=[None, 784], name="x")
    x_image = tf.reshape(x, [-1, 28, 28, 1])
    tf.summary.image('input', x_image, 3)
    y = tf.placeholder(tf.float32, shape=[None, 10], name="labels")

    if use_two_conv:
    conv1 = conv_layer(x_image, 1, 32, "conv1")
    conv_out = conv_layer(conv1, 32, 64, "conv2")
    else:
    conv1 = conv_layer(x_image, 1, 64, "conv")
    conv_out = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")

    flattened = tf.reshape(conv_out, [-1, 7 * 7 * 64])


    if use_two_fc:
    fc1 = fc_layer(flattened, 7 * 7 * 64, 1024, "fc1")
    embedding_input = fc1
    embedding_size = 1024
    logits = fc_layer(fc1, 1024, 10, "fc2")
    else:
    embedding_input = flattened
    embedding_size = 7*7*64
    logits = fc_layer(flattened, 7*7*64, 10, "fc")

    with tf.name_scope("xent"):
    xent = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(
    logits=logits, labels=y), name="xent")
    tf.summary.scalar("xent", xent)

    with tf.name_scope("train"):
    train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)

    with tf.name_scope("accuracy"):
    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    tf.summary.scalar("accuracy", accuracy)

    summ = tf.summary.merge_all()


    embedding = tf.Variable(tf.zeros([1024, embedding_size]), name="test_embedding")
    assignment = embedding.assign(embedding_input)
    saver = tf.train.Saver()

    sess.run(tf.global_variables_initializer())
    writer = tf.summary.FileWriter(logdir)
    writer.add_graph(sess.graph)

    config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
    embedding_config = config.embeddings.add()
    embedding_config.tensor_name = embedding.name
    embedding_config.sprite.image_path = '/tmp/mnist_tutorial/sprite_1024.png'
    embedding_config.metadata_path = '/tmp/mnist_tutorial/labels_1024.tsv'
    # Specify the width and height of a single thumbnail.
    embedding_config.sprite.single_image_dim.extend([28, 28])
    tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config)

    for i in range(2001):
    batch = mnist.train.next_batch(100)
    if i % 5 == 0:
    [train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: batch[0], y: batch[1]})
    writer.add_summary(s, i)
    if i % 500 == 0:
    sess.run(assignment, feed_dict={x: mnist.test.images[:1024], y: mnist.test.labels[:1024]})
    saver.save(sess, os.path.join(logdir, "model.ckpt"), i)
    sess.run(train_step, feed_dict={x: batch[0], y: batch[1]})

    def make_hparam_string(learning_rate, use_two_fc, use_two_conv):
    conv_param = "conv=2" if use_two_conv else "conv=1"
    fc_param = "fc=2" if use_two_fc else "fc=1"
    return "lr_%.0E,%s,%s" % (learning_rate, conv_param, fc_param)

    def main():
    # You can try adding some more learning rates
    for learning_rate in [1E-3]:

    # You can also change to use fewer layers
    for use_two_fc in [True]:
    for use_two_conv in [True]:
    # Construct a hyperparameter string for each one (example: "lr_1E-3,fc=2,conv=2)
    hparam = make_hparam_string(learning_rate, use_two_fc, use_two_conv)
    print('Starting run for %s' % hparam)

    logdir = "/tmp/mnist_tutorial/" + hparam
    # Actually run with the new settings
    mnist_model(learning_rate, use_two_fc, use_two_conv, logdir)


    if __name__ == '__main__':
    main()