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Modified Readme for DeepSchool.io

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sachinruk committed Jul 1, 2017
1 parent 34a738d commit 6c28dcd0f2cd8260fe774ea7427f26a65b9debb7

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@@ -37,9 +37,17 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 1,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Theano backend.\n"
]
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
@@ -401,6 +409,17 @@
"### 1 Hidden Layer"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Dense?"
]
},
{
"cell_type": "code",
"execution_count": 13,
@@ -579,15 +598,6 @@
"# Set activation='relu' in all layers and padding='same', Maxpool does not have an activation\n",
"# All you need to specify is the kernel_size and filters parameters\n",
"# As a thumb rule the number of filters double. eg. choose 4, 8, 16 for the 3 layers\n",
"model.add(Conv2D(8, kernel_size=(3,3), padding='same', activation='relu', input_shape = (width,height,channels)))\n",
"model.add(MaxPool2D(pool_size=(2, 2)))\n",
"# model.add(Dropout(0.5))\n",
"model.add(Conv2D(16, kernel_size=(3,3), padding='same', activation='relu'))\n",
"model.add(MaxPool2D(pool_size=(2, 2)))\n",
"# model.add(Dropout(0.5))\n",
"model.add(Conv2D(32, kernel_size=(3,3), padding='same', activation='relu'))\n",
"model.add(MaxPool2D(pool_size=(2, 2)))\n",
"# model.add(Dropout(0.5))\n",
"model.add(Flatten())\n",
"model.add(Dense(10, activation='softmax'))\n",
"# Note: You so not apply dropout on final layer.\n",
@@ -633,6 +643,57 @@
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"32*32*3"
]
},
{
"cell_type": "code",
"execution_count": 5,
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{
"data": {
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"224"
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"metadata": {},
"output_type": "execute_result"
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"source": [
"3*3*3*8+8"
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{
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"1168"
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"source": [
"3*3*8*16+16"
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},
{
"cell_type": "code",
"execution_count": 16,
@@ -748,6 +809,17 @@
"https://www.quora.com/Why-does-batch-normalization-help"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"BatchNormalization?"
]
},
{
"cell_type": "code",
"execution_count": 36,
@@ -767,7 +839,7 @@
"# TODO: Add another set of Conv2D followed by BatchNormalization, followed by relu activation, followed by maxpool (4 lines of code)\n",
"# TODO: flatten the layer\n",
"# TODO: Add the last softmax layer\n",
"model.compile(optimizer='adadelta', loss='sparse_categorical_crossentropy')"
"model.compile(optimizer='adadelta', loss='sparse_categorical_crossentropy', metrics = ['accuracy'])"
]
},
{
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