remove jupyter notebook from repo

1 files changed, 0 insertions, 119 deletions

diff --git a/OLD/csci4511w/colab/HW5 - Neural Networks/test_cases.py b/OLD/csci4511w/colab/HW5 - Neural Networks/test_cases.py
deleted file mode 100644
index ebd8ca1..0000000
--- a/OLD/csci4511w/colab/HW5 - Neural Networks/test_cases.py
+++ /dev/null
@@ -1,119 +0,0 @@
-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