Attachment 185841 Details for Bug 221880 – tf_demo2.py

tf_demo2.py

tf_demo2.py (text/plain), 1.77 KB, created by Jov on 2017-08-28 11:54:22 UTC

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Creator: Jov

Created: 2017-08-28 11:54:22 UTC

Size: 1.77 KB

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obsolete

>import tensorflow as tf
># NumPy is often used to load, manipulate and preprocess data.
>import numpy as np
>
># Declare list of features. We only have one real-valued feature. There are many
># other types of columns that are more complicated and useful.
>features = [tf.contrib.layers.real_valued_column("x", dimension=1)]
>
># An estimator is the front end to invoke training (fitting) and evaluation
># (inference). There are many predefined types like linear regression,
># logistic regression, linear classification, logistic classification, and
># many neural network classifiers and regressors. The following code
># provides an estimator that does linear regression.
>estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)
>
># TensorFlow provides many helper methods to read and set up data sets.
># Here we use two data sets: one for training and one for evaluation
># We have to tell the function how many batches
># of data (num_epochs) we want and how big each batch should be.
>x_train = np.array([1., 2., 3., 4.])
>y_train = np.array([0., -1., -2., -3.])
>x_eval = np.array([2., 5., 8., 1.])
>y_eval = np.array([-1.01, -4.1, -7, 0.])
>input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x_train}, y_train,
>                                              batch_size=4,
>                                              num_epochs=1000)
>eval_input_fn = tf.contrib.learn.io.numpy_input_fn(
>    {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000)
>
># We can invoke 1000 training steps by invoking the  method and passing the
># training data set.
>estimator.fit(input_fn=input_fn, steps=1000)
>
># Here we evaluate how well our model did.
>train_loss = estimator.evaluate(input_fn=input_fn)
>eval_loss = estimator.evaluate(input_fn=eval_input_fn)
>print("train loss: %r"% train_loss)
>print("eval loss: %r"% eval_loss)

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Attachments on bug 221880: 185841