Add gradient visualisations

[asmith26]

Something like:

grad_linear_w = []
grad_linear_b = []
grad_linear_1_w = []
grad_linear_1_b = []
grad_linear_2_w = []
grad_linear_2_b = []
grad_linear_3_w = []
grad_linear_3_b = []

for i in range(200):  # num epochs
    grads = compute_grads()
    grad_linear_w.extend(pd.Series(grads["linear"]["w"].flatten()).tolist())
    grad_linear_b.extend(pd.Series(grads["linear"]["b"].flatten()).tolist())
    grad_linear_1_w.extend(pd.Series(grads["linear_1"]["w"].flatten()).tolist())
    grad_linear_1_b.extend(pd.Series(grads["linear_1"]["b"].flatten()).tolist())
    grad_linear_2_w.extend(pd.Series(grads["linear_2"]["w"].flatten()).tolist())
    grad_linear_2_b.extend(pd.Series(grads["linear_2"]["b"].flatten()).tolist())
    grad_linear_3_w.extend(pd.Series(grads["linear_3"]["w"].flatten()).tolist())
    grad_linear_3_b.extend(pd.Series(grads["linear_3"]["b"].flatten()).tolist())

grad_s_linear_w = pd.Series(grad_linear_w)
grad_s_linear_b = pd.Series(grad_linear_b)
grad_s_linear_1_w = pd.Series(grad_linear_1_w)
grad_s_linear_1_b = pd.Series(grad_linear_1_b)
grad_s_linear_2_w = pd.Series(grad_linear_2_w)
grad_s_linear_2_b = pd.Series(grad_linear_2_b)
grad_s_linear_3_w = pd.Series(grad_linear_3_w)
grad_s_linear_3_b = pd.Series(grad_linear_3_b)

(grad_s_linear_w.hvplot().opts(width=300, title="linear_w") +
 grad_s_linear_b.hvplot().opts(width=300, title="linear_b") +
 grad_s_linear_1_w.hvplot().opts(width=300, title="linear_1_w") +
 grad_s_linear_1_b.hvplot().opts(width=300, title="linear_1_b") +
 grad_s_linear_2_w.hvplot().opts(width=300, title="linear_2_w") +
 grad_s_linear_2_b.hvplot().opts(width=300, title="linear_2_b") +
 grad_s_linear_3_w.hvplot().opts(width=300, title="linear_3_w") +
 grad_s_linear_3_b.hvplot().opts(width=300, title="linear_3_b"))