From 9eb19b66db7b92f2dc1ba1556149fe23b9f667cd Mon Sep 17 00:00:00 2001 From: Anish Shah Date: Sun, 3 Jan 2016 21:01:25 +0530 Subject: [PATCH 1/2] Change tf.types.int16 to tf.int16 --- .../notebooks/1_intro/basic_operations.ipynb | 31 ++++++++++++------- 1 file changed, 20 insertions(+), 11 deletions(-) diff --git a/deep-learning/tensor-flow-examples/notebooks/1_intro/basic_operations.ipynb b/deep-learning/tensor-flow-examples/notebooks/1_intro/basic_operations.ipynb index 79a1b60..7a9794c 100644 --- a/deep-learning/tensor-flow-examples/notebooks/1_intro/basic_operations.ipynb +++ b/deep-learning/tensor-flow-examples/notebooks/1_intro/basic_operations.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -68,7 +68,7 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -76,8 +76,8 @@ "# The value returned by the constructor represents the output\n", "# of the Variable op. (define as input when running session)\n", "# tf Graph input\n", - "a = tf.placeholder(tf.types.int16)\n", - "b = tf.placeholder(tf.types.int16)" + "a = tf.placeholder(tf.int16)\n", + "b = tf.placeholder(tf.int16)" ] }, { @@ -194,25 +194,34 @@ " result = sess.run(product)\n", " print result" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "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, From 3a5c12bd85b9eb219e0e52b36c5ea7f7d7e649d0 Mon Sep 17 00:00:00 2001 From: Anish Shah Date: Mon, 4 Jan 2016 20:36:24 +0530 Subject: [PATCH 2/2] Change to tf.dtype --- .../notebooks/3_neural_networks/alexnet.ipynb | 18 +++++++++--------- .../convolutional_network.ipynb | 18 +++++++++--------- .../3_neural_networks/recurrent_network.ipynb | 12 ++++++------ 3 files changed, 24 insertions(+), 24 deletions(-) diff --git a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb index d3b600d..2e28a0c 100644 --- a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb +++ b/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, diff --git a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/convolutional_network.ipynb b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/convolutional_network.ipynb index 22299fc..967550a 100644 --- a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/convolutional_network.ipynb +++ b/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, diff --git a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/recurrent_network.ipynb b/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/recurrent_network.ipynb index e8a7a14..41e3e1f 100644 --- a/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/recurrent_network.ipynb +++ b/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,