minor fix

kirstenlin · Nov 18, 2015 · 3f7255a · 3f7255a
1 parent 4fbed84
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Showing 11 changed files with 807 additions and 57 deletions.
diff --git a/Setup_TensorFlow.md b/Setup_TensorFlow.md
diff --git a/examples/3 - Neural Networks/alexnet.py b/examples/3 - Neural Networks/alexnet.py
@@ -126,7 +126,7 @@ def alex_net(_X, _weights, _biases, _dropout):
             acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
             # Calculate batch loss
             loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
-            print "Iter " + str(step*batch_size) + ", Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)
+            print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)
         step += 1
     print "Optimization Finished!"
     # Calculate accuracy for 256 mnist test images

diff --git a/examples/3 - Neural Networks/convolutional_network.py b/examples/3 - Neural Networks/convolutional_network.py
@@ -105,7 +105,7 @@ def conv_net(_X, _weights, _biases, _dropout):
             acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
             # Calculate batch loss
             loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
-            print "Iter " + str(step*batch_size) + ", Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)
+            print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)
         step += 1
     print "Optimization Finished!"
     # Calculate accuracy for 256 mnist test images

diff --git a/examples/3 - Neural Networks/multilayer_perceptron.py b/examples/3 - Neural Networks/multilayer_perceptron.py
@@ -18,7 +18,7 @@
 batch_size = 100
 display_step = 1
 
-#Network Parameters
+# Network Parameters
 n_hidden_1 = 256 # 1st layer num features
 n_hidden_2 = 256 # 2nd layer num features
 n_input = 784 # MNIST data input (img shape: 28*28)

diff --git a/notebooks/1 - Introduction/basic_operations.ipynb b/notebooks/1 - Introduction/basic_operations.ipynb
@@ -195,15 +195,6 @@
     "    print result\n",
     "    # ==> [[ 12.]]"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/notebooks/1 - Introduction/helloworld.ipynb b/notebooks/1 - Introduction/helloworld.ipynb
@@ -61,15 +61,6 @@
     "# Run graph\n",
     "print sess.run(hello)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/notebooks/2 - Basic Classifiers/linear_regression.ipynb b/notebooks/2 - Basic Classifiers/linear_regression.ipynb
@@ -225,15 +225,6 @@
     "from IPython.display import Image\n",
     "Image(filename='linearreg.png')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/notebooks/2 - Basic Classifiers/logistic_regression.ipynb b/notebooks/2 - Basic Classifiers/logistic_regression.ipynb
@@ -199,15 +199,6 @@
     "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
     "    print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/notebooks/2 - Basic Classifiers/nearest_neighbor.ipynb b/notebooks/2 - Basic Classifiers/nearest_neighbor.ipynb
@@ -352,15 +352,6 @@
     "    print \"Done!\"\n",
     "    print \"Accuracy:\", accuracy"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {