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‎data_persist/jupyter/TensorFlow/GPU/.ipynb_checkpoints/Test GPU-checkpoint.ipynb

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"cells": [],
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"nbformat": 4,
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‎data_persist/jupyter/TensorFlow/GPU/Test GPU.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# The AWS/GPU situation is very annoying. \n",
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"\n",
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"#I just used this pre-built AMI:\n",
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"## https://gist.github.com/erikbern/78ba519b97b440e10640"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"usage: __main__.py [-h] [--batch_size BATCH_SIZE] [--num_batches NUM_BATCHES]\n",
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"__main__.py: error: unrecognized arguments: -f /root/.local/share/jupyter/runtime/kernel-64f89975-b1c6-4e61-86ac-9eb0feaeafe5.json\n"
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]
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},
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{
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"ename": "SystemExit",
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"evalue": "2",
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"output_type": "error",
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"traceback": [
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"An exception has occurred, use %tb to see the full traceback.\n",
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"\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 2\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"To exit: use 'exit', 'quit', or Ctrl-D.\n"
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]
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}
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],
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"source": [
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"from datetime import datetime\n",
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"import math\n",
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"import time\n",
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"\n",
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"import tensorflow.python.platform\n",
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"import tensorflow as tf\n",
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"from tensorflow.python.ops import array_ops\n",
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"\n",
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"FLAGS = tf.app.flags.FLAGS\n",
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"\n",
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"tf.app.flags.DEFINE_integer('batch_size', 128,\n",
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" \"\"\"Batch size.\"\"\")\n",
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"tf.app.flags.DEFINE_integer('num_batches', 100,\n",
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" \"\"\"Number of batches to run.\"\"\")\n",
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"\n",
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"parameters = []\n",
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"\n",
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"conv_counter = 1\n",
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"pool_counter = 1\n",
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"affine_counter = 1\n",
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"\n",
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"def _conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType):\n",
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" global conv_counter\n",
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" global parameters\n",
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" name = 'conv' + str(conv_counter)\n",
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" conv_counter += 1\n",
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" with tf.name_scope(name) as scope:\n",
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" kernel = tf.Variable(tf.truncated_normal([kH, kW, nIn, nOut],\n",
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" dtype=tf.float32,\n",
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" stddev=1e-1), name='weights')\n",
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" conv = tf.nn.conv2d(inpOp, kernel, [1, dH, dW, 1], padding=padType)\n",
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" biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32),\n",
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" trainable=True, name='biases')\n",
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" bias = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())\n",
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" conv1 = tf.nn.relu(bias, name=scope)\n",
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" parameters += [kernel, biases]\n",
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" return conv1\n",
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"\n",
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"def _affine(inpOp, nIn, nOut):\n",
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" global affine_counter\n",
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" global parameters\n",
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" name = 'affine' + str(affine_counter)\n",
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" affine_counter += 1\n",
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" with tf.name_scope(name) as scope:\n",
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" kernel = tf.Variable(tf.truncated_normal([nIn, nOut],\n",
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" dtype=tf.float32,\n",
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" stddev=1e-1), name='weights')\n",
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" biases = tf.Variable(tf.constant(0.0, shape=[nOut], dtype=tf.float32),\n",
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" trainable=True, name='biases')\n",
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" affine1 = tf.nn.relu_layer(inpOp, kernel, biases, name=name)\n",
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" parameters += [kernel, biases]\n",
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" return affine1\n",
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"\n",
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"def _mpool(inpOp, kH, kW, dH, dW, padding):\n",
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" global pool_counter\n",
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" global parameters\n",
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" name = 'pool' + str(pool_counter)\n",
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" pool_counter += 1\n",
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" return tf.nn.max_pool(inpOp,\n",
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" ksize=[1, kH, kW, 1],\n",
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" strides=[1, dH, dW, 1],\n",
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" padding=padding,\n",
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" name=name)\n",
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"\n",
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"def _apool(inpOp, kH, kW, dH, dW, padding):\n",
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" global pool_counter\n",
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" global parameters\n",
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" name = 'pool' + str(pool_counter)\n",
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" pool_counter += 1\n",
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" return tf.nn.avg_pool(inpOp,\n",
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" ksize=[1, kH, kW, 1],\n",
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" strides=[1, dH, dW, 1],\n",
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" padding=padding,\n",
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" name=name)\n",
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"\n",
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"def _inception(inp, inSize, o1s, o2s1, o2s2, o3s1, o3s2, o4s1, o4s2):\n",
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" conv1 = _conv(inp, inSize, o1s, 1, 1, 1, 1, 'SAME')\n",
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"\n",
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" conv3_ = _conv(inp, inSize, o2s1, 1, 1, 1, 1, 'SAME')\n",
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" conv3 = _conv(conv3_, o2s1, o2s2, 3, 3, 1, 1, 'SAME')\n",
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"\n",
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" conv5_ = _conv(inp, inSize, o3s1, 1, 1, 1, 1, 'SAME')\n",
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" conv5 = _conv(conv5_, o3s1, o3s2, 5, 5, 1, 1, 'SAME')\n",
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"\n",
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" pool_ = _mpool(inp, o4s1, o4s1, 1, 1, 'SAME')\n",
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" pool = _conv(pool_, inSize, o4s2, 1, 1, 1, 1, 'SAME')\n",
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"\n",
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" incept = array_ops.concat(3, [conv1, conv3, conv5, pool])\n",
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" return incept\n",
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"\n",
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"\n",
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"def loss(logits, labels):\n",
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" batch_size = tf.size(labels)\n",
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" labels = tf.expand_dims(labels, 1)\n",
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" indices = tf.expand_dims(tf.range(0, batch_size, 1), 1)\n",
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" concated = tf.concat(1, [indices, labels])\n",
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" onehot_labels = tf.sparse_to_dense(\n",
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" concated, tf.pack([batch_size, 1000]), 1.0, 0.0)\n",
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" cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits,\n",
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" onehot_labels,\n",
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" name='xentropy')\n",
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" loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')\n",
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" return loss\n",
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"\n",
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"def inference(images):\n",
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" conv1 = _conv (images, 3, 64, 7, 7, 2, 2, 'SAME')\n",
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" pool1 = _mpool(conv1, 3, 3, 2, 2, 'SAME')\n",
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" conv2 = _conv (pool1, 64, 64, 1, 1, 1, 1, 'SAME')\n",
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" conv3 = _conv (conv2, 64, 192, 3, 3, 1, 1, 'SAME')\n",
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" pool3 = _mpool(conv3, 3, 3, 2, 2, 'SAME')\n",
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"\n",
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" incept3a = _inception(pool3, 192, 64, 96, 128, 16, 32, 3, 32)\n",
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" incept3b = _inception(incept3a, 256, 128, 128, 192, 32, 96, 3, 64)\n",
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" pool4 = _mpool(incept3b, 3, 3, 2, 2, 'SAME')\n",
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" incept4a = _inception(pool4, 480, 192, 96, 208, 16, 48, 3, 64)\n",
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" incept4b = _inception(incept4a, 512, 160, 112, 224, 24, 64, 3, 64)\n",
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" incept4c = _inception(incept4b, 512, 128, 128, 256, 24, 64, 3, 64)\n",
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" incept4d = _inception(incept4c, 512, 112, 144, 288, 32, 64, 3, 64)\n",
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" incept4e = _inception(incept4d, 528, 256, 160, 320, 32, 128, 3, 128)\n",
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" pool5 = _mpool(incept4e, 3, 3, 2, 2, 'SAME')\n",
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" incept5a = _inception(pool5, 832, 256, 160, 320, 32, 128, 3, 128)\n",
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" incept5b = _inception(incept5a, 832, 384, 192, 384, 48, 128, 3, 128)\n",
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" pool6 = _apool(incept5b, 7, 7, 1, 1, 'VALID')\n",
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"\n",
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" resh1 = tf.reshape(pool6, [-1, 1024])\n",
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" affn1 = _affine(resh1, 1024, 1000)\n",
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"\n",
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" return affn1\n",
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"\n",
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"\n",
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"def time_tensorflow_run(session, target, info_string):\n",
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" num_steps_burn_in = 10\n",
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" total_duration = 0.0\n",
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" total_duration_squared = 0.0\n",
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" if not isinstance(target, list):\n",
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" target = [target]\n",
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" for i in xrange(FLAGS.num_batches + num_steps_burn_in):\n",
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" start_time = time.time()\n",
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" _ = session.run(tf.group(*target))\n",
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" duration = time.time() - start_time\n",
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" if i > num_steps_burn_in:\n",
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" if not i % 10:\n",
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" print ('%s: step %d, duration = %.3f' %\n",
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" (datetime.now(), i - num_steps_burn_in, duration))\n",
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" total_duration += duration\n",
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" total_duration_squared += duration * duration\n",
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" mn = total_duration / FLAGS.num_batches\n",
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" vr = total_duration_squared / FLAGS.num_batches - mn * mn\n",
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" sd = math.sqrt(vr)\n",
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" print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %\n",
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" (datetime.now(), info_string, FLAGS.num_batches, mn, sd))\n",
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"\n",
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"def run_benchmark():\n",
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" global parameters\n",
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" with tf.Graph().as_default():\n",
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" # Generate some dummy images.\n",
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" image_size = 224\n",
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" images = tf.Variable(tf.random_normal([FLAGS.batch_size,\n",
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" image_size,\n",
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" image_size, 3],\n",
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" dtype=tf.float32,\n",
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" stddev=1e-1))\n",
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"\n",
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" labels = tf.Variable(tf.ones([FLAGS.batch_size],\n",
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" dtype=tf.int32))\n",
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"\n",
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" # Build a Graph that computes the logits predictions from the\n",
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" # inference model.\n",
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" last_layer = inference(images)\n",
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"\n",
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" # Build an initialization operation.\n",
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" init = tf.initialize_all_variables()\n",
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"\n",
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" # Start running operations on the Graph.\n",
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" config = tf.ConfigProto()\n",
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" config.gpu_options.allocator_type = 'BFC'\n",
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" sess = tf.Session(config=config)\n",
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" sess.run(init)\n",
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"\n",
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" # Run the forward benchmark.\n",
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" time_tensorflow_run(sess, last_layer, \"Forward\")\n",
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"\n",
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" # Add a simple objective so we can calculate the backward pass.\n",
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" objective = loss(last_layer, labels)\n",
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" # Compute the gradient with respect to all the parameters.\n",
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" grad = tf.gradients(objective, parameters)\n",
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" # Run the backward benchmark.\n",
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" time_tensorflow_run(sess, grad, \"Forward-backward\")\n",
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"\n",
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"\n",
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"def main(_):\n",
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" run_benchmark()\n",
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"\n",
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"\n",
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"if __name__ == '__main__':\n",
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" tf.app.run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"display_name": "Python 2",
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"language": "python",
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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