batch gen consistency

code/ch13/ch13.ipynb

@@ -778,7 +778,7 @@
     "    training_loss = []\n",
     "    batch_generator = create_batch_generator(\n",
     "            X_train_centered, y_train, \n",
-    "            batch_size=64, shuffle=True)\n",
+    "            batch_size=64)\n",
     "    for batch_X, batch_y in batch_generator:\n",
     "        ## prepare a dict to feed data to our network:\n",
     "        feed = {tf_x:batch_X, tf_y:batch_y}\n",
@@ -1389,11 +1389,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[NbConvertApp] Converting notebook ch13.ipynb to script\n",
+      "[NbConvertApp] Writing 18184 bytes to ch13.py\n"
+     ]
+    }
+   ],
    "source": [
     "! python ../.convert_notebook_to_script.py --input ch13.ipynb --output ch13.py"
    ]

code/ch13/ch13.py

@@ -397,7 +397,7 @@ def create_batch_generator(X, y, batch_size=128, shuffle=False):
         y_copy = data[:, -1].astype(int)
 
     for i in range(0, X.shape[0], batch_size):
-        yield (X[i:i+batch_size, :], y[i:i+batch_size])
+        yield (X_copy[i:i+batch_size, :], y_copy[i:i+batch_size])
 
 
 
@@ -413,7 +413,7 @@ def create_batch_generator(X, y, batch_size=128, shuffle=False):
     training_loss = []
     batch_generator = create_batch_generator(
             X_train_centered, y_train, 
-            batch_size=64, shuffle=True)
+            batch_size=64)
     for batch_X, batch_y in batch_generator:
         ## prepare a dict to feed data to our network:
         feed = {tf_x:batch_X, tf_y:batch_y}
@@ -668,3 +668,8 @@ def tanh(z):
 
 
 
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+