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xldrx/Batch+Size+Test.ipynb

Last active October 9, 2017 23:39
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Test the effect of batch size
Display the source blob
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Batch+Size+Test.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 0.0667530298233 0.0202220570854 100\n",
"2 0.101294636726 0.0143614476756 100\n",
"4 0.159001708031 0.0122533362064 100\n",
"8 0.284222841263 0.019770890117 100\n",
"16 0.512441396713 0.0245514665774 100\n",
"32 1.05267119408 0.0333890508251 100\n",
"64 2.0679615736 0.0327217044203 100\n"
]
}
],
"source": [
"all_results = {}\n",
"for batch_size in range(7):\n",
" tf.reset_default_graph()\n",
" batch_size = 2**batch_size\n",
" inputs,outputs = tf.random_normal([batch_size, 299,299,3]), tf.random_normal([batch_size, 1000])\n",
" model,_ = nets.inception.inception_v3(inputs=inputs)\n",
" loss = tf.losses.mean_squared_error(model, outputs)\n",
" opt = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)\n",
" \n",
" with tf.train.MonitoredTrainingSession(config=tf.ConfigProto(log_device_placement=True)) as sess:\n",
" results = []\n",
" for i in range(100):\n",
" s = time.time()\n",
" sess.run(opt)\n",
" e = time.time()\n",
" results.append(e-s)\n",
" print(batch_size, np.median(results), np.std(results), len(results))\n",
" all_results[batch_size] = results"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
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