Skip to content

Added bias terms to the network #92

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Added bias terms to the network #92

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
38 changes: 19 additions & 19 deletions 04_modern_net.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,24 +16,25 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"def init_weights(shape):\n",
" return tf.Variable(tf.random_normal(shape, stddev=0.01))\n",
"\n",
"def model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden): # this network is the same as the previous one except with an extra hidden layer + dropout\n",
" X = tf.nn.dropout(X, p_keep_input)\n",
" h = tf.nn.relu(tf.matmul(X, w_h))\n",
"def init_biases(shape):\n",
" return tf.Variable(tf.zeros(shape))\n",
"\n",
"def model(X, w_h, b_h, w_h2, b_h2, w_o, b_o, p_keep_input, p_keep_hidden): # this network is the same as the previous one except with an extra hidden layer + dropout\n",
" X = tf.nn.dropout(X, p_keep_input)\n",
" h = tf.nn.relu(tf.matmul(X, w_h)) + b_h\n",
" \n",
" h = tf.nn.dropout(h, p_keep_hidden)\n",
" h2 = tf.nn.relu(tf.matmul(h, w_h2))\n",
" h2 = tf.nn.relu(tf.matmul(h, w_h2)) + b_h2\n",
"\n",
" h2 = tf.nn.dropout(h2, p_keep_hidden)\n",
"\n",
" return tf.matmul(h2, w_o)\n",
" return tf.matmul(h2, w_o) + b_o\n",
"\n",
"mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
"trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels"
Expand All @@ -42,21 +43,22 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"X = tf.placeholder(\"float\", [None, 784])\n",
"Y = tf.placeholder(\"float\", [None, 10])\n",
"\n",
"w_h = init_weights([784, 625])\n",
"b_h = init_biases([625])\n",
"w_h2 = init_weights([625, 625])\n",
"b_h2 = init_biases([625])\n",
"w_o = init_weights([625, 10])\n",
"b_o = init_biases([10])\n",
"\n",
"p_keep_input = tf.placeholder(\"float\")\n",
"p_keep_hidden = tf.placeholder(\"float\")\n",
"py_x = model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden)\n",
"py_x = model(X, w_h, b_h, w_h2, b_h2, w_o, b_o, p_keep_input, p_keep_hidden)\n",
"\n",
"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))\n",
"train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)\n",
Expand All @@ -66,9 +68,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# Launch the graph in a session\n",
Expand Down Expand Up @@ -105,16 +105,16 @@
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 1
}