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Authors: Harald Schilly, ℏal Snyder
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Description: Jupyter notebook tensorflow.ipynb
Compute Environment: Ubuntu 18.04 (Deprecated)

Tensorflow on CoCalc

Make sure to use the Jupyter Kernel "Python 3 (Anaconda or Ubuntu)"

Say Hello to Tensorflow!

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import tensorflow as tf print(tf.__version__) tf
1.9.0
<module 'tensorflow' from '/ext/anaconda5/lib/python3.6/site-packages/tensorflow/__init__.py'>
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# Import data mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True, fake_data=FLAGS.fake_data) sess = tf.InteractiveSession() # Create a multilayer model. # Input placeholders with tf.name_scope('input'): x = tf.placeholder(tf.float32, [None, 784], name='x-input') y_ = tf.placeholder(tf.float32, [None, 10], name='y-input') with tf.name_scope('input_reshape'): image_shaped_input = tf.reshape(x, [-1, 28, 28, 1]) tf.summary.image('input', image_shaped_input, 10) # We can't initialize these variables to 0 - the network will get stuck. def weight_variable(shape): """Create a weight variable with appropriate initialization.""" initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): """Create a bias variable with appropriate initialization.""" initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def variable_summaries(var): """Attach a lot of summaries to a Tensor (for TensorBoard visualization).""" with tf.name_scope('summaries'): mean = tf.reduce_mean(var) tf.summary.scalar('mean', mean) with tf.name_scope('stddev'): stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) tf.summary.scalar('stddev', stddev) tf.summary.scalar('max', tf.reduce_max(var)) tf.summary.scalar('min', tf.reduce_min(var)) tf.summary.histogram('histogram', var)
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-2-d42bbacf9517> in <module>() 1 # Import data ----> 2 mnist = input_data.read_data_sets(FLAGS.data_dir, 3 one_hot=True, 4 fake_data=FLAGS.fake_data) 5 NameError: name 'input_data' is not defined
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hello = tf.constant('Hello, TensorFlow!') sess = tf.Session() sess.run(hello)
b'Hello, TensorFlow!'
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# check your version of python import sys sys.version
'3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 13:39:56) \n[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]'
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# just some general setup import numpy as np from IPython.display import clear_output, Image, display %matplotlib inline import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = (14, 8)
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a = tf.constant(10) b = tf.constant(32) sess.run(a + b)
42
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# check your version of python import sys sys.version
'3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 13:39:56) \n[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]'
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# just some general setup import numpy as np from IPython.display import clear_output, Image, display %matplotlib inline import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = (14, 8)

Visualization Function

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def DisplayFractal(a, fmt='jpeg'): """ Display an array of iteration counts as a colorful picture of a fractal. """ a_cyclic = (2 * np.pi * a / 20.0)[:,:,np.newaxis] # .reshape(list(a.shape)+[1]) img = np.concatenate([10 + 20 * np.cos(a_cyclic), 30 + 50 * np.sin(a_cyclic), 155 - 80 * np.cos(a_cyclic)], 2) img[a==a.max()] = 0 a = img a = np.uint8(np.clip(a, 0, 255)) plt.imshow(a)
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Mandelbrot Set (from the Tutorial)

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Y, X = np.mgrid[-1.3:1.3:0.005, -2:1:0.005] Z = X+1j*Y
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sess = tf.InteractiveSession()
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xs = tf.constant(Z.astype(np.complex64)) zs = tf.Variable(xs) ns = tf.Variable(tf.zeros(Z.shape))
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tf.initialize_all_variables().run()
WARNING:tensorflow:From /ext/anaconda5/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py:118: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02. Instructions for updating: Use `tf.global_variables_initializer` instead.
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# Compute the new values of z: z^2 + x zs_ = zs*zs + xs # Have we diverged with this new value? not_diverged = tf.complex_abs(zs_) < 4 # Operation to update the zs and the iteration count. # # Note: We keep computing zs after they diverge! This # is very wasteful! There are better, if a little # less simple, ways to do this. # step = tf.group( zs.assign(zs_), ns.assign_add(tf.cast(not_diverged, "float32")) )
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-14-928785c5c96f> in <module>() 3 4 # Have we diverged with this new value? ----> 5 not_diverged = tf.complex_abs(zs_) < 4 6 7 # Operation to update the zs and the iteration count. AttributeError: module 'tensorflow' has no attribute 'complex_abs'
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%time for i in range(200): step.run()
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs Wall time: 8.58 µs
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-15-47a6b92292f6> in <module>() 1 get_ipython().run_line_magic('time', '') 2 for i in range(200): ----> 3 step.run() NameError: name 'step' is not defined
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%time DisplayFractal(ns.eval())
CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs Wall time: 5.48 µs
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sess.close()
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Julia Set

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Y, X = np.mgrid[-1.3:1.3:0.005, -1.3:1.3:0.005] Z = X + 1j*Y C = 0.12 + .63j
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sess = tf.InteractiveSession()
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xx = tf.constant(Z.astype("complex64")) jwork = tf.Variable(xx) jconst = tf.constant(np.complex64(C)) julia = tf.Variable(tf.zeros(Z.shape)) tf.initialize_all_variables().run()
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jwork_ = jwork*jwork + jconst not_diverged = tf.complex_abs(jwork_) < 4 julia_step = tf.group( jwork.assign(jwork_), julia.assign_add(tf.cast(not_diverged, "float32")) )
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-21-0d3a8d5a8cbe> in <module>() 1 jwork_ = jwork*jwork + jconst 2 ----> 3 not_diverged = tf.complex_abs(jwork_) < 4 4 5 julia_step = tf.group( AttributeError: module 'tensorflow' has no attribute 'complex_abs'
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%time for i in range(200): julia_step.run()
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%time DisplayFractal(julia.eval())
CPU times: user 2 µs, sys: 1 µs, total: 3 µs Wall time: 6.2 µs
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sess.close()
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import random from sklearn import datasets, cross_validation, metrics from sklearn import preprocessing import skflow skflow
--------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-24-72ff285e2262> in <module>() 1 import random 2 ----> 3 from sklearn import datasets, cross_validation, metrics 4 from sklearn import preprocessing 5 ImportError: cannot import name 'cross_validation'
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random.seed(42) # Load dataset boston = datasets.load_boston() X, y = boston.data, boston.target # Split dataset into train / test X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2, random_state=42) # scale data (training set) to 0 mean and unit Std. dev scaler = preprocessing.StandardScaler() X_train = scaler.fit_transform(X_train) # Build 2 layer fully connected DNN with 10, 10 units respecitvely. regressor = skflow.TensorFlowDNNRegressor(hidden_units=[10, 10], steps=5000, learning_rate=0.1, batch_size=1) # Fit regressor.fit(X_train, y_train) # Predict and score score = metrics.mean_squared_error(regressor.predict(scaler.fit_transform(X_test)), y_test) print('MSE: {0:f}'.format(score))
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with tf.Session() as sess: x = tf.Variable(21) tf.initialize_all_variables().run() tf.group(x.assign(x + x)).run() print(x.eval())
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