Merge branch 'master' of https://github.com/astorfi/TensorFlow-World

.travis.yml

@@ -9,8 +9,11 @@ python:
 install:
   - pip install -r requirements.txt
   - pip install coveralls
+  - pip install flake8
 
 script:
+  # stop the build if there are Python syntax errors or undefined names
+  - flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics
 
   - coverage run --omit=*.virtualenvs*,*virtualenv* codes/0-welcome/code/0-welcome.py test
   - coverage run --omit=*.virtualenvs*,*virtualenv* codes/1-basics/basic_math_operations/code/basic_math_operation.py test
@@ -20,9 +23,6 @@ script:
   - coverage run --omit=*.virtualenvs*,*virtualenv* codes/2-basics_in_machine_learning/logistic_regression/code/logistic_regression.py test
   - coverage run --omit=*.virtualenvs*,*virtualenv* codes/2-basics_in_machine_learning/multiclass_svm/code/multiclass_svm.py test
 
-
-
-
 after_success:
   coveralls
 

codes/2-basics_in_machine_learning/linear_regression/code/linear_regression.py

@@ -1,5 +1,4 @@
 import numpy as np
-import matplotlib.pyplot as plt
 import tensorflow as tf
 import xlrd
 import matplotlib.pyplot as plt
@@ -16,8 +15,7 @@
 #######################
 ## Defining flags #####
 #######################
-tf.app.flags.DEFINE_integer(
-    'num_epochs', 5, 'The number of epochs for training the model. Default=50')
+tf.app.flags.DEFINE_integer('num_epochs', 50, 'The number of epochs for training the model. Default=50')
 # Store all elemnts in FLAG structure!
 FLAGS = tf.app.flags.FLAGS
 
@@ -58,7 +56,7 @@ def loss(X, Y):
 
     # Making the prediction.
     Y_predicted = inference(X)
-    return tf.squared_difference(Y, Y_predicted)
+    return tf.reduce_sum(tf.squared_difference(Y, Y_predicted))/(2*data.shape[0])
 
 
 # The training function.
@@ -81,11 +79,8 @@ def train(loss):
 
     # Step 8: train the model
     for epoch_num in range(FLAGS.num_epochs): # run 100 epochs
-        for x, y in data:
-          train_op = train(train_loss)
-
-          # Session runs train_op to minimize loss
-          loss_value,_ = sess.run([train_loss,train_op], feed_dict={X: x, Y: y})
+        loss_value, _ = sess.run([train_loss,train_op],
+                                 feed_dict={X: data[:,0], Y: data[:,1]})
 
         # Displaying the loss per epoch.
         print('epoch %d, loss=%f' %(epoch_num+1, loss_value))

codes/3-neural_networks/multi-layer-perceptron/code/train_mlp.py

@@ -221,7 +221,7 @@
         test_summary_writer = tf.summary.FileWriter(test_summary_dir)
         test_summary_writer.add_graph(sess.graph)
 
-        # If fie-tuning flag in 'True' the model will be restored.
+        # If fine-tuning flag in 'True' the model will be restored.
         if FLAGS.fine_tuning:
             saver.restore(sess, os.path.join(FLAGS.checkpoint_root, checkpoint_prefix))
             print("Model restored for fine-tuning...")

docs/tutorials/0-welcome/README.rst

@@ -80,27 +80,5 @@ The ``session``, which is the environment for running the operations, is execute
     sess.close()
 
 The ``tf.summary.FileWriter`` is defined to write the summaries into ``event files``.The command of ``sess.run()`` must be used for evaluation of any ``Tensor`` otherwise the operation won't be executed. In the end by using the ``writer.close()``, the summary writer will be closed.
-
---------
-Results
---------
-
-The results for running in the terminal is as bellow:
-
-.. code:: shell
-
-        a = 5.0
-        b = 10.0
-        a + b = 15.0
-        a/b = 0.5
-
-
-
-If we run the Tensorboard using ``tensorboard --logdir="absolute/path/to/log_dir"`` we get the following when visualiaing the ``Graph``:
-
-.. figure:: https://github.com/astorfi/TensorFlow-World/blob/master/docs/_img/0-welcome/graph-run.png
-   :scale: 30 %
-   :align: center
-
-   **Figure 1:** The TensorFlow Graph.
+