completed part 2 implementing gradient descent

python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/multilayer.py

@@ -0,0 +1,38 @@
+import numpy as np
+
+
+def sigmoid(x):
+    """
+    Calculate sigmoid
+    """
+    return 1 / (1 + np.exp(-x))
+
+
+# Network size
+N_input = 4
+N_hidden = 3
+N_output = 2
+
+np.random.seed(42)
+# Make some fake data
+X = np.random.randn(4)
+
+weights_input_to_hidden = np.random.normal(
+    0, scale=0.1, size=(N_input, N_hidden))
+weights_hidden_to_output = np.random.normal(
+    0, scale=0.1, size=(N_hidden, N_output))
+
+
+# TODO: Make a forward pass through the network
+
+hidden_layer_in = np.dot(X, weights_input_to_hidden)
+hidden_layer_out = sigmoid(hidden_layer_in)
+
+print('Hidden-layer Output:')
+print(hidden_layer_out)
+
+output_layer_in = np.dot(hidden_layer_out, weights_hidden_to_output)
+output_layer_out = sigmoid(output_layer_in)
+
+print('Output-layer Output:')
+print(output_layer_out)