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implemented conv2d of mnist from scratch added fourth week notes

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Malar Kannan
2017-10-28 17:46:00 +05:30
parent 64b89c326b
commit e792d29ba1
6 changed files with 354 additions and 2 deletions
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7
ThirdSunday/Faces.py
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@@ -65,14 +65,17 @@ def create_model(input_dim,output_dim):
error_lower_bound = tf.constant(0.9,name='lower_bound')
x = tf.placeholder(tf.float32, [None,input_dim])
y = tf.placeholder(tf.float32, [None,output_dim])
W1 = tf.Variable(tf.random_normal([input_dim, 512],stddev=2.0/math.sqrt(input_dim)),name='layer_1_weights')
b1 = tf.Variable(tf.random_normal([512]),name='bias_1_weights')
layer_1 = tf.nn.relu(tf.add(tf.matmul(x,W1),b1))
W2 = tf.Variable(tf.random_normal([512, 128],stddev=2.0/math.sqrt(512)),name='layer_2_weights')
b2 = tf.Variable(tf.random_normal([128]),name='bias_2_weights')
layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1,W2),b2))
W_o = tf.Variable(tf.random_normal([128, output_dim],stddev=2.0/math.sqrt(128)),name='layer_output_weights')
b_o = tf.Variable(tf.random_normal([output_dim]),name='bias_output_weights')
layer_1 = tf.nn.relu(tf.add(tf.matmul(x,W1),b1))
layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1,W2),b2))
#y_ = tf.nn.softmax(tf.add(tf.matmul(layer_2,W_o),b_o))+1e-6
y_ = tf.add(tf.matmul(layer_2,W_o),b_o)#tf.nn.relu()