Clean up this scratch space

numpy_examples/dropout.py

@@ -0,0 +1,25 @@
+""" 
+Inverted Dropout: Recommended implementation example.
+We drop and scale at train time and don't do anything at test time.
+"""
+
+p = 0.5 # probability of keeping a unit active. higher = less dropout
+
+def train_step(X):
+  # forward pass for example 3-layer neural network
+  H1 = np.maximum(0, np.dot(W1, X) + b1)
+  U1 = (np.random.rand(*H1.shape) < p) / p # first dropout mask. Notice /p!
+  H1 *= U1 # drop!
+  H2 = np.maximum(0, np.dot(W2, H1) + b2)
+  U2 = (np.random.rand(*H2.shape) < p) / p # second dropout mask. Notice /p!
+  H2 *= U2 # drop!
+  out = np.dot(W3, H2) + b3
+  
+  # backward pass: compute gradients... (not shown)
+  # perform parameter update... (not shown)
+  
+def predict(X):
+  # ensembled forward pass
+  H1 = np.maximum(0, np.dot(W1, X) + b1) # no scaling necessary
+  H2 = np.maximum(0, np.dot(W2, H1) + b2)
+  out = np.dot(W3, H2) + b3