Change to tf.dtype

Author: AnishShah

deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb

@@ -86,9 +86,9 @@
    "outputs": [],
    "source": [
     "# tf Graph input\n",
-    "x = tf.placeholder(tf.types.float32, [None, n_input])\n",
-    "y = tf.placeholder(tf.types.float32, [None, n_classes])\n",
-    "keep_prob = tf.placeholder(tf.types.float32) # dropout (keep probability)"
+    "x = tf.placeholder(tf.float32, [None, n_input])\n",
+    "y = tf.placeholder(tf.float32, [None, n_classes])\n",
+    "keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)"
    ]
   },
   {
@@ -218,7 +218,7 @@
    "source": [
     "# Evaluate model\n",
     "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n",
-    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.types.float32))"
+    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
    ]
   },
   {
@@ -323,21 +323,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.5+"
   }
  },
  "nbformat": 4,

deep-learning/tensor-flow-examples/notebooks/3_neural_networks/convolutional_network.ipynb

@@ -86,9 +86,9 @@
    "outputs": [],
    "source": [
     "# tf Graph input\n",
-    "x = tf.placeholder(tf.types.float32, [None, n_input])\n",
-    "y = tf.placeholder(tf.types.float32, [None, n_classes])\n",
-    "keep_prob = tf.placeholder(tf.types.float32) #dropout (keep probability)"
+    "x = tf.placeholder(tf.float32, [None, n_input])\n",
+    "y = tf.placeholder(tf.float32, [None, n_classes])\n",
+    "keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)"
    ]
   },
   {
@@ -201,7 +201,7 @@
    "source": [
     "# Evaluate model\n",
     "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n",
-    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.types.float32))"
+    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
    ]
   },
   {
@@ -299,21 +299,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.5+"
   }
  },
  "nbformat": 4,

deep-learning/tensor-flow-examples/notebooks/3_neural_networks/recurrent_network.ipynb

@@ -137,7 +137,7 @@
     "\n",
     "# Evaluate model\n",
     "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n",
-    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.types.float32))"
+    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
    ]
   },
   {
@@ -272,21 +272,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.5+"
   }
  },
  "nbformat": 4,