From dd13d8348859093b76326ca5a28fe338918579d9 Mon Sep 17 00:00:00 2001
From: RossTheRoss <mstrapp@protonmail.com>
Date: Fri, 30 Apr 2021 08:41:36 -0500
Subject: Add HW5 to git

---
 .../colab/HW5 - Neural Networks/test_cases.py      | 119 +++++++++++++++++++++
 1 file changed, 119 insertions(+)
 create mode 100644 csci4511w/colab/HW5 - Neural Networks/test_cases.py

(limited to 'csci4511w/colab/HW5 - Neural Networks/test_cases.py')

diff --git a/csci4511w/colab/HW5 - Neural Networks/test_cases.py b/csci4511w/colab/HW5 - Neural Networks/test_cases.py
new file mode 100644
index 0000000..ebd8ca1
--- /dev/null
+++ b/csci4511w/colab/HW5 - Neural Networks/test_cases.py	
@@ -0,0 +1,119 @@
+import numpy as np
+
+def layer_sizes_test_case():
+    np.random.seed(1)
+    X_assess = np.random.randn(5, 3)
+    Y_assess = np.random.randn(2, 3)
+    return X_assess, Y_assess
+
+def initialize_parameters_test_case():
+    n_x, n_h, n_y = 2, 4, 1
+    return n_x, n_h, n_y
+
+
+def forward_propagation_test_case():
+    np.random.seed(1)
+    X_assess = np.random.randn(2, 3)
+    b1 = np.random.randn(4,1)
+    b2 = np.array([[ -1.3]])
+
+    parameters = {'W1': np.array([[-0.00416758, -0.00056267],
+        [-0.02136196,  0.01640271],
+        [-0.01793436, -0.00841747],
+        [ 0.00502881, -0.01245288]]),
+     'W2': np.array([[-0.01057952, -0.00909008,  0.00551454,  0.02292208]]),
+     'b1': b1,
+     'b2': b2}
+
+    return X_assess, parameters
+
+def compute_cost_test_case():
+    np.random.seed(1)
+    Y_assess = (np.random.randn(1, 3) > 0)
+    parameters = {'W1': np.array([[-0.00416758, -0.00056267],
+        [-0.02136196,  0.01640271],
+        [-0.01793436, -0.00841747],
+        [ 0.00502881, -0.01245288]]),
+     'W2': np.array([[-0.01057952, -0.00909008,  0.00551454,  0.02292208]]),
+     'b1': np.array([[ 0.],
+        [ 0.],
+        [ 0.],
+        [ 0.]]),
+     'b2': np.array([[ 0.]])}
+
+    a2 = (np.array([[ 0.5002307 ,  0.49985831,  0.50023963]]))
+
+    return a2, Y_assess, parameters
+
+def backward_propagation_test_case():
+    np.random.seed(1)
+    X_assess = np.random.randn(2, 3)
+    Y_assess = (np.random.randn(1, 3) > 0)
+    parameters = {'W1': np.array([[-0.00416758, -0.00056267],
+        [-0.02136196,  0.01640271],
+        [-0.01793436, -0.00841747],
+        [ 0.00502881, -0.01245288]]),
+     'W2': np.array([[-0.01057952, -0.00909008,  0.00551454,  0.02292208]]),
+     'b1': np.array([[ 0.],
+        [ 0.],
+        [ 0.],
+        [ 0.]]),
+     'b2': np.array([[ 0.]])}
+
+    cache = {'A1': np.array([[-0.00616578,  0.0020626 ,  0.00349619],
+         [-0.05225116,  0.02725659, -0.02646251],
+         [-0.02009721,  0.0036869 ,  0.02883756],
+         [ 0.02152675, -0.01385234,  0.02599885]]),
+  'A2': np.array([[ 0.5002307 ,  0.49985831,  0.50023963]]),
+  'Z1': np.array([[-0.00616586,  0.0020626 ,  0.0034962 ],
+         [-0.05229879,  0.02726335, -0.02646869],
+         [-0.02009991,  0.00368692,  0.02884556],
+         [ 0.02153007, -0.01385322,  0.02600471]]),
+  'Z2': np.array([[ 0.00092281, -0.00056678,  0.00095853]])}
+    return parameters, cache, X_assess, Y_assess
+
+def update_parameters_test_case():
+    parameters = {'W1': np.array([[-0.00615039,  0.0169021 ],
+        [-0.02311792,  0.03137121],
+        [-0.0169217 , -0.01752545],
+        [ 0.00935436, -0.05018221]]),
+ 'W2': np.array([[-0.0104319 , -0.04019007,  0.01607211,  0.04440255]]),
+ 'b1': np.array([[ -8.97523455e-07],
+        [  8.15562092e-06],
+        [  6.04810633e-07],
+        [ -2.54560700e-06]]),
+ 'b2': np.array([[  9.14954378e-05]])}
+
+    grads = {'dW1': np.array([[ 0.00023322, -0.00205423],
+        [ 0.00082222, -0.00700776],
+        [-0.00031831,  0.0028636 ],
+        [-0.00092857,  0.00809933]]),
+ 'dW2': np.array([[ -1.75740039e-05,   3.70231337e-03,  -1.25683095e-03,
+          -2.55715317e-03]]),
+ 'db1': np.array([[  1.05570087e-07],
+        [ -3.81814487e-06],
+        [ -1.90155145e-07],
+        [  5.46467802e-07]]),
+ 'db2': np.array([[ -1.08923140e-05]])}
+    return parameters, grads
+
+def nn_model_test_case():
+    np.random.seed(1)
+    X_assess = np.random.randn(2, 3)
+    Y_assess = (np.random.randn(1, 3) > 0)
+    return X_assess, Y_assess
+
+def predict_test_case():
+    np.random.seed(1)
+    X_assess = np.random.randn(2, 3)
+    parameters = {'W1': np.array([[-0.00615039,  0.0169021 ],
+        [-0.02311792,  0.03137121],
+        [-0.0169217 , -0.01752545],
+        [ 0.00935436, -0.05018221]]),
+     'W2': np.array([[-0.0104319 , -0.04019007,  0.01607211,  0.04440255]]),
+     'b1': np.array([[ -8.97523455e-07],
+        [  8.15562092e-06],
+        [  6.04810633e-07],
+        [ -2.54560700e-06]]),
+     'b2': np.array([[  9.14954378e-05]])}
+    return parameters, X_assess
-- 
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