hw1.ipynb

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      "source": [
        "# Problem Set 1 (96 points) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "db9ZNU737iiW"
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      "source": [
        "## Important information\n",
        "1. We provide signatures of the functions that you have to implement. Make sure you follow the signatures defined, otherwise your coding solutions will not be graded.\n",
        "\n",
        "2. Please submit the single Jupyter Notebook file, where only Python and Markdown/$\\LaTeX$ are used. Any hand-written solutions inserted by photos or in any other way **are prohibitive and will not be graded**. If you will have any questions about using Markdown, ask them!  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6NtqoPnY7iiX"
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      "source": [
        "# Problem 1 (Theoretical tasks)  (36 pts)\n",
        "\n",
        "_1._\n",
        "- (1 pts) What are the constants $C_1$ and $C_2$ such that $C_1 \\|x\\|_{1} \\leq \\|x\\|_{\\infty} \\leq C_2 \\| x\\|_{1}$ for any vector $x$\n",
        "- (5 pts) Prove that $\\| U A \\|_F = \\| A U \\|_F = \\| A \\|_F$ for any unitary matrix $U$.\n",
        "- (5 pts) Prove that $\\| U A \\|_2 = \\| A U \\|_2 = \\| A \\|_2$ for any unitary matrix $U$.\n",
        "  \n",
        "_2._\n",
        "- (5 pts) Using the results from the previous subproblem, prove that $\\| A \\|_F \\le \\sqrt{\\mathrm{rank}(A)} \\| A \\|_2$. _Hint:_ SVD will help you.\n",
        "- (5 pts) Show that for any $m, n$ and $k \\le \\min(m, n)$ there exists $A \\in \\mathbb{R}^{m \\times n}: \\mathrm{rank}(A) = k$, such that $\\| A \\|_F = \\sqrt{\\mathrm{rank}(A)} \\| A \\|_2$. In other words, show that the previous inequality is not strict.\n",
        "- (5 pts) Prove that if $A \\in \\mathbb{R}^{n \\times n}$ - diagonalizable matrix, then $\\det(\\exp(A)) = \\exp(\\mathrm{trace}(A))$. \n",
        "\n",
        "_Hint:_ $\\exp(S^{-1}AS) = \\sum\\limits_{k=0}^{\\infty}\\frac{(S^{-1}AS)^k}{k!} = S^{-1}\\exp(A)S$, moreover $\\exp(D) = \\mathrm{diag}(\\exp(d_1), \\exp(d_2), ..., \\exp(d_n))$ where $D = \\mathrm{diag}(d_1, d_2, ..., d_n)$ - diagonal matrix with diagonal values $d_1, d_2, ..., d_n$\n",
        "\n",
        "\n",
        "- (5 pts) Prove that $\\| A B \\|_2 \\le \\| A \\|_2 \\| B \\|_F$.\n",
        "\n",
        "_3._\n",
        "\n",
        "- (2 pts) Let $U \\in \\mathbb{C}^{n \\times k}, k < n$ be a matrix so that $U^*U = I_k$. \n",
        "Find a pseudoinverse of $UU^*$\n",
        "- (3 pts) Compute  pseudoinverse of the matrix analytically \n",
        "\n",
        "$$\n",
        "A = \\begin{bmatrix}\n",
        "2\\\\\n",
        "3\\\\\n",
        "\\vdots\\\\\n",
        "\\sqrt{5n-1}\n",
        "\\end{bmatrix}\n",
        "\\begin{bmatrix}\n",
        "-1 & 1 & -1 & \\ldots & (-1)^{n}\n",
        "\\end{bmatrix}\n",
        "$$"
      ]
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      "source": [
        "1.\n",
        "* Let's consider x as a vector in n-dimension space: $x = (x_1, ..., x_n)$.\n",
        "In this case $\\sum_{i=1}^n |x_i| \\le n \\cdot \\max_i |x_i| = n\\|x\\|_{\\infty}\\Rightarrow C_1 = \\frac{1}{n}$.\n",
        "\n",
        "  Obviously that $\\max_i |x_i| \\in \\sum_{i=1}^n |x_i|$ so $C_2 = 1$.\n",
        "\n",
        "  As a result: $\\frac{1}{n} \\|x\\|_{1} \\leq \\|x\\|_{\\infty} \\leq  \\| x\\|_{1}$\n",
        "\n",
        "  *if $n = \\infty$ or it isn't known we can take $C_1=0$*\n",
        "*   We know that $\\|A\\|_{F} = \\sqrt{\\langle A, A\\rangle_F} = \\sqrt{\\mathrm{trace}(A^*A)}$.\n",
        "\n",
        "  $\\Rightarrow \\| U A \\|_F = \\sqrt{\\mathrm{trace}((UA)^*(UA))} = \\sqrt{\\mathrm{trace}(A^* U^* UA)} = \\sqrt{\\mathrm{trace}(A^* A)} = \\|A\\|_{F}$\n",
        "\n",
        "  And the same:  $\\| AU \\|_F = \\sqrt{\\mathrm{trace}((AU)^*(AU))} = \\sqrt{\\mathrm{trace}((U^*)(A^* AU))} = \\sqrt{\\mathrm{trace}((A^* AU)(U^*))} = \\sqrt{\\mathrm{trace}(A^* AUU^*)} = \\sqrt{\\mathrm{trace}(A^* A)} = \\|A\\|_{F}$\n",
        "\n",
        "  We used $U^*U = UU^* = I$ and $\\mathrm{trace}(AB)=\\mathrm{trace}(BA)$.\n",
        "*   $[def]\\ \\| A \\|_2 = \\sqrt{\\lambda_{max}(A^* A)} \\Rightarrow \\| UA \\|_2 = \\sqrt{\\lambda_{max}((UA)^* (UA))} = \\sqrt{\\lambda_{max}(A^* U^* UA)} = \\sqrt{\\lambda_{max}(A^* A)}= \\| A \\|_2 $\n",
        "\n",
        "  $\\Rightarrow \\| U^* A^* \\|_2  = \\|A^*\\|_2$ since $U^*$ - also unitary.\n",
        "\n",
        "  $\\| AU \\|_2 = \\sqrt{\\lambda_{max}((AU)^* (AU))} = \\sqrt{\\lambda_{max}(U^* A^* AU)} = \\sqrt{\\lambda_{max}(U^* A^* (U^* A^*)^*)}=\\| (U^* A^*)^* \\|_2 = \\| A \\|_2$\n"
      ]
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      "source": [
        "2.\n",
        "\n",
        "\n",
        "*   Let's use SVD of matrix $A$: $A = U\\Sigma V^* \\Rightarrow \\| A \\|_F = \\| U\\Sigma V^* \\|_F = \\sqrt{\\mathrm{trace}((U\\Sigma V^*)^*(U\\Sigma V^*))} = \\sqrt{\\mathrm{trace}(( V \\Sigma^* U^*)(U\\Sigma V^*))} = \\sqrt{\\mathrm{trace}(V \\Sigma^* \\Sigma V^*)} = \\| \\Sigma \\|_F = \\sqrt{\\sum_i \\sigma_i^2}$\n",
        "\n",
        "  Among $\\sigma_i$ only $\\mathrm{rank}(A)$ elements aren't equal to 0 and $\\| A \\|_2 = \\| \\Sigma \\|_2 = \\max_i \\sigma_i \\Rightarrow \\| A \\|_F = \\sqrt{\\sum_i \\sigma_i^2} \\le \\sqrt{\\mathrm{rank}(A)\\cdot (\\max_i \\sigma_i)^2} = \\sqrt{\\mathrm{rank}(A)} \\| A \\|_2$.\n",
        "*   To obtain an equality we should take $A$ such that $\\sigma_i = \\sigma_j$ for all $i,j$. E.g. we can take diagonal $A$ with equal diagonal elements.\n",
        "*   $A \\in \\mathbb{R}^{n \\times n}$ - diagonalizable matrix, then $S^{-1}AS = D$ where $D$ - diagonal for some $S$ with $\\det(S) \\ne 0$.\n",
        "\n",
        "  $\\exp(S^{-1}AS) = \\sum\\limits_{k=0}^{\\infty}\\frac{(S^{-1}AS)^k}{k!} = S^{-1}\\exp(A)S = \\exp(D) = \\mathrm{diag}(\\exp(d_1), \\exp(d_2), ..., \\exp(d_n))$\n",
        "\n",
        "  $\\Rightarrow \\det(S^{-1}\\exp(A)S) = \\det(\\mathrm{diag}(\\exp(d_1), ..., \\exp(d_n))) = \\prod_{i=1}^n \\exp(d_i)$\n",
        "\n",
        "  $\\Rightarrow \\det(S^{-1})\\cdot\\det(\\exp(A))\\cdot\\det(S) = \\prod_{i=1}^n \\exp(d_i)$ \n",
        "\n",
        "  because of $\\det(S^{-1})\\cdot\\det(S) = 1 \\Rightarrow \\det(\\exp(A)) = \\prod_{i=1}^n \\exp(d_i)$\n",
        "\n",
        "  $\\exp(\\mathrm{trace}(S^{-1}AS)) = \\exp(\\sum_{i=1}^n d_i) = \\prod_{i=1}^n \\exp(d_i)$\n",
        "\n",
        "  because of $\\mathrm{trace}(S^{-1}AS) = \\mathrm{trace}(ASS^{-1}) = \\mathrm{trace}(A) \\Rightarrow \\exp(\\mathrm{trace}(A)) = \\prod_{i=1}^n \\exp(d_i)$\n",
        "\n",
        "  $\\Rightarrow \\det(\\exp(A)) = \\exp(\\mathrm{trace}(A))$\n",
        "*   $B = U \\Sigma V^* \\Rightarrow \\| B \\|_F = \\| \\Sigma \\|_F = \\sqrt{\\sum_i\\sigma_i^2} \\ge \\sqrt{\\max_i \\sigma_i^2} = \\| \\Sigma \\|_2 = \\| B \\|_2 \\Rightarrow \\| A B \\|_2 \\le \\| A \\|_2 \\| B \\|_2 \\le \\| A \\|_2 \\| B \\|_F$\n",
        "  "
      ]
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      "source": [
        "3.\n",
        "\n",
        "\n",
        "*   Because of $U^*U = I_k \\Rightarrow \\mathrm{rank}(I_k) \\le \\mathrm{rank}(U) \\Rightarrow \\mathrm{rank}(U) = k$. We can consider $UU^*$ as matrix decomposition of some $A = UU^*$. So $A^+ = (U^*)^+ U^+$.\n",
        "\n",
        "  Let's find $U^+$: $UU^+ U=U,\\ U^+=VU^*$ for some $V$\n",
        "\n",
        "  $\\Rightarrow UVU^* U=U \\Rightarrow U^* UVU^* U=U^* U \\Rightarrow V = I_k$\n",
        "\n",
        "  $\\Rightarrow U^+=I_kU^* = U^*$ \n",
        "\n",
        "  Let's find $(U^*)^+$: $U^*(U^*)^+ U^*=U^*,\\ (U^*)^+=VU$ for some $V$\n",
        "\n",
        "  $\\Rightarrow U^*(U^*)^+ U^*U=U^*U \\Rightarrow U^*(U^*)^+=I_k \\Rightarrow (U^*)^+= U$\n",
        "\n",
        "  $\\Rightarrow A^+=UU^*$ \n",
        "\n",
        "*  \n",
        "$$\n",
        "A = \\begin{bmatrix}\n",
        "2\\\\\n",
        "3\\\\\n",
        "\\vdots\\\\\n",
        "\\sqrt{5n-1}\n",
        "\\end{bmatrix}\n",
        "\\begin{bmatrix}\n",
        "-1 & 1 & -1 & \\ldots & (-1)^{n}\n",
        "\\end{bmatrix}\n",
        "$$\n",
        "We can obtain it as $A^+ = C^+ B^+$ where $$ B = \\begin{bmatrix} 2\\\\ 3\\\\ \\vdots\\\\ \\sqrt{5n-1} \\end{bmatrix} $$,\n",
        "$$ C = \\begin{bmatrix}\n",
        "-1 & 1 & -1 & \\ldots & (-1)^{n}\n",
        "\\end{bmatrix} $$\n",
        "$B^+ = (B^* B)^{-1}B^*$ (it can be achieved as above but in general case) $B^* = B^T \\Rightarrow B^TB = \\sum_n (5n - 1) = \\frac{5n(n+1)}{2} - n = \\frac{n(5n + 3)}{2} \\Rightarrow (B^TB)^{-1} = \\frac{2}{n(5n + 3)}$\n",
        "$$ B^+ = \\frac{2}{n(5n + 3)}\\begin{bmatrix}\n",
        "2\\ 3\\ \\ldots\\ \\sqrt{5n-1}\n",
        "\\end{bmatrix}\n",
        "$$\n",
        "$C^+ = C^T(C C^T)^{-1} \\Rightarrow C C^T =n \\Rightarrow (CC^T)^{-1} = \\frac{1}{n}$\n",
        "$$ C^+ = \\frac{1}{n}\\begin{bmatrix}\n",
        "1\\\\\n",
        "-1\\\\\n",
        "\\vdots\\\\\n",
        "(-1)^{n}\n",
        "\\end{bmatrix}\n",
        "$$\n",
        "$$ A^+ = \\frac{2}{n^2(5n + 3)}\\begin{bmatrix}\n",
        "2 & 3 & \\ldots & \\sqrt{5n-1}\\\\\n",
        "-2 & -3 & \\ldots & -\\sqrt{5n-1}\\\\\n",
        "\\vdots\\\\\n",
        "(-1)^{n}\\cdot 2 & (-1)^{n}\\cdot 3 & \\ldots & (-1)^{n}\\cdot\\sqrt{5n-1}\\\\\n",
        "\\end{bmatrix}\n",
        "$$\n",
        "\n"
      ]
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      "source": [
        "# Problem 2 (Matrix calculus) (15 pts)\n",
        "\n",
        "_1._ (11 pts) Consider the following function\n",
        "\n",
        "$$ F(U, V) = \\frac{1}{2}\\|X - UDV\\|_F^2, $$\n",
        "\n",
        "where $X \\in \\mathbb{R}^{n \\times n}$, $U \\in \\mathbb{R}^{n \\times k}$, $V \\in \\mathbb{R}^{k \\times n}$, $k < n$ and $D = diag(d_1, \\ldots, d_k)$ is given diagonal matrix.\n",
        "\n",
        "- (2 pts) Derive analytical expression for the gradient of the function $F$ with respect to $U$ \n",
        "- (2 pts) Derive analytical expression for the gradient of the function $F$ with respect to $V$\n",
        "- (7 pts) Estimate computational complexity of computing these gradients (in big-O notation). Also, compare timing of analytical computations versus timing the automatic differentiation with JAX. Study some range of dimensions to extract asymptotic complexity and make a conclusion, what approach is faster. Plot the dependence of running time on the dimension (row and column separately) to proof your conclusion \n",
        "\n",
        "_2._ (4 pts) Derive analytical expression for the gradient and hessian of the function $f$:\n",
        "\n",
        "$$ R(x) = \\frac{(Ax, x)}{(x, x)}, $$\n",
        "\n",
        "where $A$ is a symmetric real matrix. Why the gradient of this function is important in NLA you will know in the lectures later."
      ]
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      "source": [
        "1. *In this case $X^* = X^T$ but I still use $^*$*\n",
        "\n",
        "\n",
        "*   $F(U, V) = \\frac{1}{2}\\mathrm{trace}((X - UDV)^* (X - UDV)) = \\frac{1}{2}\\mathrm{trace}((X^* - V^* D^* U^*)(X - UDV)) = \\frac{1}{2}\\mathrm{trace}(X^* X - V^* D^* U^* X - X^* UDV + V^* D^* U^* UDV) = \\frac{1}{2}\\big[\\mathrm{trace}(X^* X) - \\mathrm{trace}(V^* D^* U^* X) - \\mathrm{trace}(X^* UDV) + \\mathrm{trace}(V^* D^* U^* UDV)\\big]$\n",
        "\n",
        "  $[D_{U_0}F](H) = \\frac{1}{2}\\big[ - \\mathrm{trace}([D_{U_0}V^* D^* U^* X](H)) - \\mathrm{trace}([D_{U_0}X^* UDV](H)) + \\mathrm{trace}([D_{U_0}V^* D^* U^* UDV](H))\\big] = \\frac{1}{2}\\big[ - \\mathrm{trace}(V^* D^* H^* X) - \\mathrm{trace}(X^* HDV) + \\mathrm{trace}(V^* D^* H^* U_0DV) +  \\mathrm{trace}(V^* D^* U^*_0 HDV)\\big] = \\frac{1}{2}\\big[ - \\mathrm{trace}(X V^* D^* H^*) - \\mathrm{trace}(XV^* D^* H^*) + \\mathrm{trace}(U_0DV V^* D^* H^* ) +  \\mathrm{trace}(U_0DVV^*D^*H^*)\\big]=\\frac{1}{2}\\mathrm{trace}[(-2X V^* D^* + 2U_0DV V^* D^*)H^*] = \\mathrm{trace}[(-X V^* D^* + U_0DV V^* D^*)H^*]$\n",
        "\n",
        "  $\\Rightarrow \\nabla_{U_0}F = -X V^* D^* + U_0DV V^* D^* = (-X + U_0DV)V^* D^*$\n",
        "\n",
        "*   $[D_{V_0}F](H) = \\frac{1}{2}\\big[ - \\mathrm{trace}([D_{V_0}V^* D^* U^* X](H)) - \\mathrm{trace}([D_{V_0}X^* UDV](H)) + \\mathrm{trace}([D_{V_0}V^* D^* U^* UDV](H))\\big] = \\frac{1}{2}\\big[ - \\mathrm{trace}(H^* D^* U^* X) - \\mathrm{trace}(X^* UDH) + \\mathrm{trace}(H^* D^* U^* UDV_0) +  \\mathrm{trace}(V_0^* D^* U^* UDH)\\big] = \\frac{1}{2}\\big[ - \\mathrm{trace}(D^* U^* X H^*) - \\mathrm{trace}(D^* U^* X H^*) + \\mathrm{trace}(D^* U^* UDV_0 H^* ) +  \\mathrm{trace}(D^* U^* UDV_0H^*)\\big] = \\mathrm{trace}[(-D^* U^* X + D^* U^* UDV_0)H^*]$\n",
        "\n",
        "  $\\Rightarrow \\nabla_{V_0}F = -D^* U^* X + D^* U^* UDV_0 = D^* U^*(-X + UDV_0)$\n",
        "\n",
        "*  For $DV$ we will need $kn$ multiplications (for each element in $V$ only 1).\n",
        "\n",
        "  For $U_0DV$ - $n^2k$ multiplications and $n^2(k-1)$ additions.\n",
        "  \n",
        "  For $-X + U_0DV$ - $n^2$ additions.\n",
        "\n",
        "  Since $V^* D^* = (DV)^*$ we can just save previous result.\n",
        "\n",
        "  And at last computation we will need $n^2k$ multiplications and $nk(n-1)$ additions.\n",
        "\n",
        "  Overall $O(kn^2 + kn)$ multiplications and $O(2n^2k-kn)$ additions.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Sv8w7RyG7iia"
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      "source": [
        "import numpy as np\n",
        "from numba import jit\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm.notebook import tqdm\n",
        "%matplotlib inline\n",
        "\n",
        "\n",
        "def matmul(a, b):\n",
        "    n = a.shape[0]\n",
        "    k = a.shape[1]\n",
        "    m = b.shape[1]  \n",
        "    c = np.zeros((n, m))\n",
        "    for i in range(n):\n",
        "        for j in range(m):\n",
        "            for s in range(k):\n",
        "                c[i, j] += a[i, s] * b[s, j]\n",
        "                \n",
        "    return c\n",
        "\n",
        "\n",
        "@jit(nopython=True)\n",
        "def numba_matmul(a, b):\n",
        "    n = a.shape[0]\n",
        "    k = a.shape[1]\n",
        "    m = b.shape[1]\n",
        "    c = np.zeros((n, m))\n",
        "    for i in range(n):\n",
        "        for j in range(m):\n",
        "            for s in range(k):\n",
        "                c[i, j] += a[i, s] * b[s, j]\n",
        "    return c\n",
        "\n",
        "\n",
        "def compute_grad_matmul(x, u, v, d):\n",
        "  dv = np.zeros((d.shape[0], v.shape[1]))\n",
        "  for i in range(d.shape[0]):  # d - diagonal \n",
        "    for j in range(v.shape[1]):\n",
        "      dv[i,j] = d[i,i]*v[i,j]\n",
        "  dvt = dv.T\n",
        "  udv = matmul(u, dv)\n",
        "  return matmul(-x + udv, dvt)\n",
        "\n",
        "\n",
        "@jit(nopython=True)\n",
        "def compute_grad_numba(x, u, v, d):\n",
        "  dv = np.zeros((d.shape[0], v.shape[1]))\n",
        "  for i in range(d.shape[0]):  # d - diagonal \n",
        "    for j in range(v.shape[1]):\n",
        "      dv[i,j] = d[i,i]*v[i,j]\n",
        "  dvt = dv.T\n",
        "  udv = numba_matmul(u, dv)\n",
        "  return numba_matmul(-x + udv, dvt)\n",
        "\n",
        "\n",
        "def compute_grad(x, u, v, d):\n",
        "  dv = d @ v\n",
        "  dvt = dv.T\n",
        "  udv = u @ dv\n",
        "  return (-x + udv) @ dvt\n",
        "\n",
        "\n",
        "def compute_grad_np(x, u, v, d):\n",
        "  dv = np.dot(d, v)\n",
        "  dvt = dv.T\n",
        "  udv = np.dot(u, dv)\n",
        "  return np.dot((-x + udv), dvt)"
      ]
    },
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        "id": "e5IcedYtRqHe",
        "outputId": "269fb85e-643d-4432-a962-6441ebcb4bc1"
      },
      "outputs": [
        {
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              "model_id": "bbf685892d644f669321d577a5f5b9c7",
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            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
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      ],
      "source": [
        "n_range = [10*i for i in range(1, 20)]\n",
        "time_range_matmul = []\n",
        "time_range_numba_matmul = []\n",
        "time_range_at = []\n",
        "time_range_np = []\n",
        "for n in tqdm(n_range):\n",
        "    k = n // 2\n",
        "    x = np.random.randn(n, n)\n",
        "    u = np.random.randn(n, k)\n",
        "    v = np.random.randn(k, n)\n",
        "    d = np.ones((k,k))  \n",
        "    t = %timeit -o -q compute_grad_matmul(x, u, v, d)\n",
        "    time_range_matmul.append(t.best)\n",
        "    t = %timeit -o -q compute_grad_numba(x, u, v, d)\n",
        "    time_range_numba_matmul.append(t.best)\n",
        "    t = %timeit -o -q compute_grad(x, u, v, d)\n",
        "    time_range_at.append(t.best)\n",
        "    t = %timeit -o -q compute_grad_np(x, u, v, d)\n",
        "    time_range_np.append(t.best)"
      ]
    },
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      "cell_type": "code",
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(n_range, time_range_matmul, label=\"Matmul\")\n",
        "plt.plot(n_range, time_range_numba_matmul, label=\"Matmul Numba\")\n",
        "plt.plot(n_range, time_range_at, label=\"@\")\n",
        "plt.plot(n_range, time_range_np, label=\"Numpy\")\n",
        "plt.legend(fontsize=15)\n",
        "plt.title(\"For different n with k = 0.5n\")\n",
        "plt.xlabel(\"Dimension for n\", fontsize=18)\n",
        "plt.ylabel(\"Time\", fontsize=18)\n",
        "plt.yscale(\"log\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "410d09ffb993473180c8bc5fdd4e921d",
            "832518d848b44032b13cb7e65e63aba0",
            "1060d06de87a4c6cbced7c92b3953b7d",
            "6283be58cd494700858b13b8057c4061",
            "c6ee2d1c7b29434ab85f06e8a49bee66",
            "4d1bb58d54a24135be284f7733dac567",
            "98bac6203cdd4b9aa583ae48f5bf4b67",
            "67b2d1499aad4507b95e7275ede8de58",
            "b3f68d51c400478393f54893be6f0b0a",
            "8b75183d1e0a432ba59880a647deae0a",
            "f3f4c9040da14ec3ad71d58e44f76c59"
          ]
        },
        "id": "3J-nrgscc3Md",
        "outputId": "d50e20cc-4dd8-43a5-8baf-1fe969cca8fb"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "410d09ffb993473180c8bc5fdd4e921d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "n = 10\n",
        "time_range_matmul = []\n",
        "time_range_numba_matmul = []\n",
        "time_range_at = []\n",
        "time_range_np = []\n",
        "for k in tqdm(range(1, n)):\n",
        "    x = np.random.randn(n, n)\n",
        "    u = np.random.randn(n, k)\n",
        "    v = np.random.randn(k, n)\n",
        "    d = np.ones((k,k))  \n",
        "    t = %timeit -o -q compute_grad_matmul(x, u, v, d)\n",
        "    time_range_matmul.append(t.best)\n",
        "    t = %timeit -o -q compute_grad_numba(x, u, v, d)\n",
        "    time_range_numba_matmul.append(t.best)\n",
        "    t = %timeit -o -q compute_grad(x, u, v, d)\n",
        "    time_range_at.append(t.best)\n",
        "    t = %timeit -o -q compute_grad_np(x, u, v, d)\n",
        "    time_range_np.append(t.best)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "xEbFCNs9j3sg",
        "outputId": "c78e8fcc-8278-4c7e-a9e3-0f2f641c4bc9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(range(1, n), time_range_matmul, label=\"Matmul\")\n",
        "plt.plot(range(1, n), time_range_numba_matmul, label=\"Matmul Numba\")\n",
        "plt.plot(range(1, n), time_range_at, label=\"@\")\n",
        "plt.plot(range(1, n), time_range_np, label=\"Numpy\")\n",
        "plt.legend(fontsize=15)\n",
        "plt.title(\"For different k with n = 50\")\n",
        "plt.xlabel(\"Dimension for k\", fontsize=18)\n",
        "plt.ylabel(\"Time\", fontsize=18)\n",
        "plt.yscale(\"log\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-PbheHeVUb00"
      },
      "source": [
        "2.\n",
        "$\\big[D_{x_0}\\frac{(Ax, x)}{(x, x)}\\big](h) = \\frac{\\big[D_{x_0}(Ax, x)\\big](h)(x_0, x_0)-(Ax_0, x_0)\\big[D_{x_0}(x, x)\\big](h)}{\\| x_0 \\|_2^4}=  \\frac{\\big[\\big([D_{x_0}Ax](h), x_0\\big)+\\big(Ax_0,[D_{x_0}x](h)\\big)\\big](x_0, x_0)-(Ax_0, x_0)\\big[\\big([D_{x_0}x](h), x_0\\big)+\\big(x_0,[D_{x_0}x](h)\\big)\\big]}{\\| x_0 \\|_2^4} = \\frac{\\big[\\big(Ah, x_0\\big)+\\big(Ax_0,h\\big)\\big](x_0, x_0)-(Ax_0, x_0)\\big[\\big(h, x_0\\big)+\\big(x_0,h\\big)\\big]}{\\| x_0 \\|_2^4} = \\frac{\\big(A^Tx_0 + Ax_0, h\\big)\\| x_0 \\|_2^2-2(Ax_0, x_0)\\big(x_0,h\\big)}{\\| x_0 \\|_2^4} =   \\frac{\\big((A^T + A)x_0, h\\big)\\| x_0 \\|_2^2-2(Ax_0, x_0)\\big(x_0,h\\big)}{\\| x_0 \\|_2^4}$\n",
        "\n",
        "$\\Rightarrow \\nabla_{x_0}R = \\frac{(A^T + A)x_0}{\\| x_0 \\|_2^2} - \\frac{2(Ax_0, x_0)x_0}{\\| x_0 \\|_2^4}$\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xf6NgP9i7iib"
      },
      "source": [
        "# Problem 3. Compression of the fully-connected layers in neural network with simple architecture (30 pts)\n",
        "\n",
        "In this problem we consider the neural network that performs classification of the dataset of images. \n",
        "Any neural network can be considered as composition of simple linear and non-linear functions.\n",
        "For example, a neural network with 3 layers can be represented as \n",
        "\n",
        "$$f_3(f_2(f_1(x, w_1), w_2), w_3),$$\n",
        "\n",
        "where $x$ is input data (in our case it will be images) and $w_i, \\; i =1,\\dots,3$ are parameters that are going to be trained. \n",
        "\n",
        "We will study the compression potential of neural network with simple architecture: alternating some numbers of linear and non-linear functions. \n",
        "\n",
        "The main task in this problem is to study how the compression of fully-connected layers affects the test accuracy.\n",
        "Any fully-connected layer is represented as linear function $AX + B$, where $X$ is input matrix and $A, B$ are trainable matrices. Matrices $A$ in every layer are going to be compressed.\n",
        "The main result that you should get is the plot of dependence of test accuracy on the total number of parameters in the neural network."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMI9Px0E7iic"
      },
      "source": [
        "#### Zero step: install PyTorch\n",
        "\n",
        "- Follow the steps in [official instructions](https://pytorch.org/get-started/locally/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfibYQXQ7iic"
      },
      "source": [
        "#### First step: download CIFAR10 dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "6e561dcd00e14be888b13998d5c79b74",
            "8db37738fbd54469a7cfd68e86d8d17b",
            "8c50b651d1de441380598c433fac2f50",
            "bd3d9c08c5554641a374da30dc99c286",
            "dc62f748026e44dfafaf88ec2cbad4a5",
            "c75be0f7b3ee476388209524ac879b65",
            "73ff1a5b9bd84da7a160aee1be4a827a",
            "55d40adfa86149849e01799ac6e3912f",
            "edab37a7737b407993bc671f451d26c6",
            "c2a6e24dcf9a46fbad75dff7fed72cd6",
            "743a56448ba644a288558add21fda011"
          ]
        },
        "id": "Z3YA1JoP7iic",
        "outputId": "94a4a3d7-e559-434e-ac83-e3a6a5ea635b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar-10-python.tar.gz\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6e561dcd00e14be888b13998d5c79b74",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ./cifar-10-python.tar.gz to ./\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision import datasets, transforms\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "\n",
        "batch_size = 100\n",
        "\n",
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(datasets.CIFAR10('./', train=True, download=True, transform=transform), \n",
        "                                        batch_size=batch_size, shuffle=True)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(datasets.CIFAR10('./', train=False, transform=transform), \n",
        "                                          batch_size=batch_size, shuffle=True)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IhK9-a147iie"
      },
      "source": [
        "#### Check what images are we going to classify"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "b9Rc0wxj7iie",
        "outputId": "bedd1fab-067c-4d64-e331-0ff242e7ae15"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  dog  ship plane  ship   car  frog   car  frog\n"
          ]
        }
      ],
      "source": [
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    plt.figure(figsize=(20, 10))\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# get some random training images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "# print labels\n",
        "print(' '.join('%5s' % classes[labels[j]] for j in range(8)))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27Kdm2SU7iif"
      },
      "source": [
        "### Second step: neural network architecture\n",
        "\n",
        "For simplicity and demonstration purposes of the neural network compression idea consider the architecture consisting of the only fully-connected layers and non-linear ReLU functions between them.\n",
        "To demonstrate compression effect, consider the dimension of the inner layers equals to 1000.\n",
        "\n",
        "Below you see implementation of such neural network in PyTorch.\n",
        "More details about neural networks you will study in the *Deep learning* course in one of the upcoming term"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "GlbvRuaG7iif"
      },
      "outputs": [],
      "source": [
        "class ResidualBlock(nn.Module):\n",
        "    def __init__(self, in_features, middle_features, activation=nn.ReLU()):\n",
        "        super().__init__()\n",
        "        self.layer1 = nn.Linear(in_features, middle_features)\n",
        "        self.layer2 = nn.Linear(middle_features, in_features)\n",
        "        self.activation = activation\n",
        "        \n",
        "    def forward(self, x):\n",
        "        out = self.layer1(x)\n",
        "        out = self.activation(out)\n",
        "        out = self.layer2(out)\n",
        "        out = out + x\n",
        "        out = self.activation(out)\n",
        "        return out\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(3 * 32 * 32, 1000)\n",
        "        self.res1 = ResidualBlock(1000,1000)\n",
        "        self.res2 = ResidualBlock(1000,1000)\n",
        "        self.fc2 = nn.Linear(1000, 10)\n",
        "        self.ReLU = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.fc1(x.view(-1, 3 * 32*32))\n",
        "        x = self.ReLU(x)\n",
        "        x = self.res1(x)\n",
        "        x = self.res2(x)\n",
        "        x = self.fc2(x)\n",
        "        return F.log_softmax(x, dim=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQDNc33w7iig"
      },
      "source": [
        "#### Implement functions for training and testing after every sweep over all dataset entries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Gy8z4AvE7iig"
      },
      "outputs": [],
      "source": [
        "def train(model, train_loader, optimizer, epoch):\n",
        "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "    model.train()\n",
        "    model.to(device)\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "        loss = F.nll_loss(output, target)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        if batch_idx % log_interval == 0:\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
        "                100. * batch_idx / len(train_loader), loss.item()))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "hKRbv9097iig"
      },
      "outputs": [],
      "source": [
        "def test(model, test_loader, accuracy=[]):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "            test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss\n",
        "            pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "    accuracy.append(100. * correct / len(test_loader.dataset))\n",
        "\n",
        "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x8UswjR77iih"
      },
      "source": [
        "### Set parameters for training and print intermediate loss values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "kVs1x_1W7iih"
      },
      "outputs": [],
      "source": [
        "log_interval = 50\n",
        "epochs = 7"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uZ8k4OgE7iih"
      },
      "source": [
        "### Third step: run training with the [Adam](https://arxiv.org/pdf/1412.6980.pdf%20%22%20entire%20document) optimization method\n",
        "\n",
        "If your laptop is not very fast, you will wait some time till training is finished. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1rsRdM3X7iii",
        "outputId": "1eaf08b3-3d6a-4ee7-879e-e605c808af7b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Epoch: 1 [0/50000 (0%)]\tLoss: 2.281185\n",
            "Train Epoch: 1 [5000/50000 (10%)]\tLoss: 1.854682\n",
            "Train Epoch: 1 [10000/50000 (20%)]\tLoss: 1.938092\n",
            "Train Epoch: 1 [15000/50000 (30%)]\tLoss: 1.586687\n",
            "Train Epoch: 1 [20000/50000 (40%)]\tLoss: 1.651476\n",
            "Train Epoch: 1 [25000/50000 (50%)]\tLoss: 1.688172\n",
            "Train Epoch: 1 [30000/50000 (60%)]\tLoss: 1.560998\n",
            "Train Epoch: 1 [35000/50000 (70%)]\tLoss: 1.594505\n",
            "Train Epoch: 1 [40000/50000 (80%)]\tLoss: 1.589302\n",
            "Train Epoch: 1 [45000/50000 (90%)]\tLoss: 1.701259\n",
            "\n",
            "Test set: Average loss: 1.4673, Accuracy: 4836/10000 (48%)\n",
            "\n",
            "Train Epoch: 2 [0/50000 (0%)]\tLoss: 1.343987\n",
            "Train Epoch: 2 [5000/50000 (10%)]\tLoss: 1.324923\n",
            "Train Epoch: 2 [10000/50000 (20%)]\tLoss: 1.457309\n",
            "Train Epoch: 2 [15000/50000 (30%)]\tLoss: 1.470368\n",
            "Train Epoch: 2 [20000/50000 (40%)]\tLoss: 1.298549\n",
            "Train Epoch: 2 [25000/50000 (50%)]\tLoss: 1.488268\n",
            "Train Epoch: 2 [30000/50000 (60%)]\tLoss: 1.414340\n",
            "Train Epoch: 2 [35000/50000 (70%)]\tLoss: 1.536619\n",
            "Train Epoch: 2 [40000/50000 (80%)]\tLoss: 1.266932\n",
            "Train Epoch: 2 [45000/50000 (90%)]\tLoss: 1.426963\n",
            "\n",
            "Test set: Average loss: 1.3925, Accuracy: 5064/10000 (51%)\n",
            "\n",
            "Train Epoch: 3 [0/50000 (0%)]\tLoss: 1.109705\n",
            "Train Epoch: 3 [5000/50000 (10%)]\tLoss: 1.313515\n",
            "Train Epoch: 3 [10000/50000 (20%)]\tLoss: 1.300171\n",
            "Train Epoch: 3 [15000/50000 (30%)]\tLoss: 1.310611\n",
            "Train Epoch: 3 [20000/50000 (40%)]\tLoss: 1.057034\n",
            "Train Epoch: 3 [25000/50000 (50%)]\tLoss: 1.186492\n",
            "Train Epoch: 3 [30000/50000 (60%)]\tLoss: 1.407112\n",
            "Train Epoch: 3 [35000/50000 (70%)]\tLoss: 1.233374\n",
            "Train Epoch: 3 [40000/50000 (80%)]\tLoss: 1.377909\n",
            "Train Epoch: 3 [45000/50000 (90%)]\tLoss: 1.301478\n",
            "\n",
            "Test set: Average loss: 1.3483, Accuracy: 5292/10000 (53%)\n",
            "\n",
            "Train Epoch: 4 [0/50000 (0%)]\tLoss: 1.154495\n",
            "Train Epoch: 4 [5000/50000 (10%)]\tLoss: 1.068586\n",
            "Train Epoch: 4 [10000/50000 (20%)]\tLoss: 0.931632\n",
            "Train Epoch: 4 [15000/50000 (30%)]\tLoss: 1.249178\n",
            "Train Epoch: 4 [20000/50000 (40%)]\tLoss: 1.436848\n",
            "Train Epoch: 4 [25000/50000 (50%)]\tLoss: 0.955968\n",
            "Train Epoch: 4 [30000/50000 (60%)]\tLoss: 1.050674\n",
            "Train Epoch: 4 [35000/50000 (70%)]\tLoss: 1.198219\n",
            "Train Epoch: 4 [40000/50000 (80%)]\tLoss: 1.234612\n",
            "Train Epoch: 4 [45000/50000 (90%)]\tLoss: 1.144114\n",
            "\n",
            "Test set: Average loss: 1.3830, Accuracy: 5263/10000 (53%)\n",
            "\n",
            "Train Epoch: 5 [0/50000 (0%)]\tLoss: 0.990809\n",
            "Train Epoch: 5 [5000/50000 (10%)]\tLoss: 0.975110\n",
            "Train Epoch: 5 [10000/50000 (20%)]\tLoss: 1.090421\n",
            "Train Epoch: 5 [15000/50000 (30%)]\tLoss: 1.085398\n",
            "Train Epoch: 5 [20000/50000 (40%)]\tLoss: 1.019159\n",
            "Train Epoch: 5 [25000/50000 (50%)]\tLoss: 1.146333\n",
            "Train Epoch: 5 [30000/50000 (60%)]\tLoss: 1.138293\n",
            "Train Epoch: 5 [35000/50000 (70%)]\tLoss: 1.190980\n",
            "Train Epoch: 5 [40000/50000 (80%)]\tLoss: 0.903417\n",
            "Train Epoch: 5 [45000/50000 (90%)]\tLoss: 1.035980\n",
            "\n",
            "Test set: Average loss: 1.3844, Accuracy: 5358/10000 (54%)\n",
            "\n",
            "Train Epoch: 6 [0/50000 (0%)]\tLoss: 0.881950\n",
            "Train Epoch: 6 [5000/50000 (10%)]\tLoss: 0.907267\n",
            "Train Epoch: 6 [10000/50000 (20%)]\tLoss: 0.876541\n",
            "Train Epoch: 6 [15000/50000 (30%)]\tLoss: 0.734545\n",
            "Train Epoch: 6 [20000/50000 (40%)]\tLoss: 0.918882\n",
            "Train Epoch: 6 [25000/50000 (50%)]\tLoss: 0.887875\n",
            "Train Epoch: 6 [30000/50000 (60%)]\tLoss: 0.879323\n",
            "Train Epoch: 6 [35000/50000 (70%)]\tLoss: 1.117719\n",
            "Train Epoch: 6 [40000/50000 (80%)]\tLoss: 0.986877\n",
            "Train Epoch: 6 [45000/50000 (90%)]\tLoss: 0.974415\n",
            "\n",
            "Test set: Average loss: 1.3981, Accuracy: 5374/10000 (54%)\n",
            "\n",
            "Train Epoch: 7 [0/50000 (0%)]\tLoss: 0.898804\n",
            "Train Epoch: 7 [5000/50000 (10%)]\tLoss: 0.801132\n",
            "Train Epoch: 7 [10000/50000 (20%)]\tLoss: 0.979291\n",
            "Train Epoch: 7 [15000/50000 (30%)]\tLoss: 0.811757\n",
            "Train Epoch: 7 [20000/50000 (40%)]\tLoss: 0.693375\n",
            "Train Epoch: 7 [25000/50000 (50%)]\tLoss: 0.757068\n",
            "Train Epoch: 7 [30000/50000 (60%)]\tLoss: 0.859197\n",
            "Train Epoch: 7 [35000/50000 (70%)]\tLoss: 0.791003\n",
            "Train Epoch: 7 [40000/50000 (80%)]\tLoss: 0.870361\n",
            "Train Epoch: 7 [45000/50000 (90%)]\tLoss: 0.913062\n",
            "\n",
            "Test set: Average loss: 1.4885, Accuracy: 5328/10000 (53%)\n",
            "\n",
            "CPU times: user 11min 31s, sys: 7 s, total: 11min 38s\n",
            "Wall time: 11min 36s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "model = Net()\n",
        "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n",
        "\n",
        "for epoch in range(1, epochs + 1):\n",
        "    train(model,  train_loader, optimizer, epoch)\n",
        "    test(model, test_loader)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7D2P48Cz7iii"
      },
      "source": [
        "Now we have somehow trained neural network and we are ready to perform compression of the weigths in the fully-connected layers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZVVScdRf7iii"
      },
      "source": [
        "- (3 pts) Compute SVD of the matrix $1000 \\times 1000$, which corresponds to a weight matrix $A$ in each layer of residual blocks. To find more information about accessing this matrix please refer to [PyTorch manual](https://pytorch.org/docs/stable/index.html).\n",
        "Plot decaying of the singular values like it was shown in the lecture. What conclusion can you make?\n",
        "- (17 pts) Create a new model, which is an analogue to the class ```Net```, but with some significant distinctions. \n",
        "It takes as input parameters the instance of the class ```Net``` and compression rank $r > 0$. \n",
        "After that, this model has to compress all matrices $A$ in fully-connected layers with SVD using first $r$ singular vectors and singular values.\n",
        "Pay attention to efficient storing of compress representation of the layers.\n",
        "Also ```forward``` method of your new residual block has to be implemented in a way to use compressed representation of the fully-connected layers (inside it) in the most efficient way. In all other aspects it has to reproduce ```forward``` method in the original non-compressed model (number of layers, activations, loss function etc).\n",
        "- (5 pts) Plot dependence of test accuracy on the number of parameters in the compressed model. This number of parameters obviously depends on the compression rank $r$.\n",
        "Also plot dependence of time to compute inference on the compression rank $r$.\n",
        "Explain obtained results.\n",
        "To measure time, use [%timeit](https://docs.python.org/3.6/library/timeit.html) with necessary parameters (examples of using this command see in lectures) \n",
        "\n",
        "- (5 pts) After such transformations, your model depends on the factors obtained from SVD. Therefore, these factors are also can be trained with the same gradient method during some number of epochs. This procedure is called **fine-tuning**. We ask you make this fine-tuning of your compressed model during from 1 to 5 epochs and compare the result test accuracy with the test accuracy that you get after compression. Explain the observed results.\n",
        "\n",
        "    **Hint 1**. Please, check that during fine-tuning, factors are updated. If they are not updated according to some reason, please check that their parameter ```requires_grad``` is setted to ```True```.\n",
        "    \n",
        "    **Hint 2**. You can use class ```torch.nn.Parameter``` to convert factors to parameters of the network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IAALapOv2DnH"
      },
      "source": [
        "1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "id": "NIDwQjYi7iij",
        "outputId": "04f58d9d-9333-42be-df10-63683364556a"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1872x1080 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "matrices = []\n",
        "\n",
        "for param in model.parameters():\n",
        "  if param.size() == torch.Size([1000, 1000]):\n",
        "    matrices.append(param.data)\n",
        "\n",
        "sing = []\n",
        "sing += [torch.linalg.svd(matrices[0]).S / torch.linalg.svd(matrices[0]).S[0]]\n",
        "sing += [torch.linalg.svd(matrices[1]).S / torch.linalg.svd(matrices[1]).S[0]]\n",
        "sing += [torch.linalg.svd(matrices[2]).S / torch.linalg.svd(matrices[2]).S[0]]\n",
        "sing += [torch.linalg.svd(matrices[3]).S / torch.linalg.svd(matrices[3]).S[0]]\n",
        "\n",
        "fig, ax = plt.subplots(2, 2, figsize=(26, 15))\n",
        "ax[0,0].set_title('Block 1 layer 1')\n",
        "ax[0,1].set_title('Block 1 layer 2')\n",
        "ax[1,0].set_title('Block 2 layer 1')\n",
        "ax[1,1].set_title('Block 2 layer 2')\n",
        "\n",
        "for i in range(4):\n",
        "  ax[i // 2, i % 2].semilogy(sing[i].cpu(), 'x')\n",
        "  ax[i // 2, i % 2].set_xlabel(r'Singular value index, $i$', fontsize=18)\n",
        "  ax[i // 2, i % 2].set_ylabel(r'$\\sigma_i / \\sigma_0$', fontsize=18)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CJOlx33GjT0E"
      },
      "source": [
        "Seems like we can cut singular values at ~200."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TVvHXCkO2IBT"
      },
      "source": [
        "2. \"Pre\"-model, the real model is below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "nsd68UU1NJXH"
      },
      "outputs": [],
      "source": [
        "state_dict = model.state_dict()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "jzYdXr1AdVWF"
      },
      "outputs": [],
      "source": [
        "class CompNet(nn.Module):\n",
        "    def __init__(self, net, r):\n",
        "        super(CompNet, self).__init__()\n",
        "        self.net =  net\n",
        "        matrices = []\n",
        "        for param in net.parameters():\n",
        "          if param.size() == torch.Size([1000, 1000]):\n",
        "            U, S, Vh = torch.linalg.svd(param.data)\n",
        "            matrices.append(torch.matmul(torch.matmul(U[:, :r], torch.diag(S[:r,])),(Vh.T[:, :r]).T))\n",
        "        self.net.res1.layer1.weight = nn.parameter.Parameter(matrices[0])\n",
        "        self.net.res1.layer2.weight = nn.parameter.Parameter(matrices[1])\n",
        "        self.net.res2.layer1.weight = nn.parameter.Parameter(matrices[2])\n",
        "        self.net.res2.layer2.weight = nn.parameter.Parameter(matrices[3])  \n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.net.forward(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tsvmuVjvqHdU"
      },
      "outputs": [],
      "source": [
        "accuracy = []\n",
        "time = []\n",
        "\n",
        "for r in tqdm([1, 20, 40, 80, 160, 320, 640, 1000]):\n",
        "  the_model =  Net()\n",
        "  the_model.load_state_dict(state_dict)\n",
        "  comp_model = CompNet(the_model, r)\n",
        "  acc = []\n",
        "  t = %timeit -o -q test(comp_model, test_loader, acc)\n",
        "  accuracy.append(np.max(acc))\n",
        "  time.append(t.best)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8b8ajvBl2Okp"
      },
      "source": [
        "3."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "v9DUU9ruyA9g",
        "outputId": "52008e38-0dfd-41e6-a996-a0a6783d10a6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, 2, figsize=(20, 7))\n",
        "ax[0].set_title('Accuracy')\n",
        "ax[1].set_title('Time')\n",
        "\n",
        "ax[0].plot([1, 20, 40, 80, 160, 320, 640, 1000], accuracy)\n",
        "ax[0].set_xlabel(\"r\", fontsize=18)\n",
        "ax[0].set_ylabel(\"accuracy, %\", fontsize=18)\n",
        "ax[1].plot([1, 20, 40, 80, 160, 320, 640, 1000], time)\n",
        "ax[1].set_xlabel(\"r\", fontsize=18)\n",
        "ax[1].set_ylabel(\"time\", fontsize=18)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3H2-V_dZhuk",
        "outputId": "c86d72c3-b5f4-4c54-851a-edbc7d869b3e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "80"
            ]
          },
          "execution_count": 118,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "[1, 20, 40, 80, 160, 320, 640, 1000][np.argmax(accuracy)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5oFRamgrUHmR"
      },
      "source": [
        "So with 80 singular values accuracy seems to be ok. Time doesn't matter here because we still multiply 1000x1000 matrices."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uYZEEN4p2U5C"
      },
      "source": [
        "4. New model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ofUyLQeRlM2M"
      },
      "outputs": [],
      "source": [
        "class CompNetR(nn.Module):\n",
        "    def __init__(self, net, r):\n",
        "        super(CompNetR, self).__init__()\n",
        "        self.net =  net\n",
        "        self.res1 = np.full((2,3), fill_value=None)\n",
        "        self.res2 = np.full((2,3), fill_value=None)\n",
        "        U, S, Vh = torch.linalg.svd(net.res1.layer1.weight)\n",
        "        b = the_model.res1.layer1.bias\n",
        "        Ur = U[:, :r]\n",
        "        Sr = S[:r,]\n",
        "        Vr = Vh.T[:, :r]\n",
        "        self.res1[0,0] = nn.Linear(1000, r, bias=False)\n",
        "        self.res1[0,0].weight = nn.parameter.Parameter(Ur.T)\n",
        "        self.res1[0,1] = nn.Linear(r, r, bias=False)\n",
        "        self.res1[0,1].weight = nn.parameter.Parameter(torch.diag(Sr))\n",
        "        self.res1[0,2] = nn.Linear(r, 1000)\n",
        "        self.res1[0,2].weight = nn.parameter.Parameter(Vr)\n",
        "        self.res1[0,2].bias = b\n",
        "        U, S, Vh = torch.linalg.svd(net.res1.layer2.weight)\n",
        "        b = the_model.res1.layer2.bias\n",
        "        Ur = U[:, :r]\n",
        "        Sr = S[:r,]\n",
        "        Vr = Vh.T[:, :r]\n",
        "        self.res1[1,0] = nn.Linear(1000, r, bias=False)\n",
        "        self.res1[1,0].weight = nn.parameter.Parameter(Ur.T)\n",
        "        self.res1[1,1] = nn.Linear(r, r, bias=False)\n",
        "        self.res1[1,1].weight = nn.parameter.Parameter(torch.diag(Sr))\n",
        "        self.res1[1,2] = nn.Linear(r, 1000)\n",
        "        self.res1[1,2].weight = nn.parameter.Parameter(Vr)\n",
        "        self.res1[1,2].bias = b\n",
        "        U, S, Vh = torch.linalg.svd(net.res2.layer1.weight)\n",
        "        b = the_model.res2.layer1.bias\n",
        "        Ur = U[:, :r]\n",
        "        Sr = S[:r,]\n",
        "        Vr = Vh.T[:, :r]\n",
        "        self.res2[0,0] = nn.Linear(1000, r, bias=False)\n",
        "        self.res2[0,0].weight = nn.parameter.Parameter(Ur.T)\n",
        "        self.res2[0,1] = nn.Linear(r, r, bias=False)\n",
        "        self.res2[0,1].weight = nn.parameter.Parameter(torch.diag(Sr))\n",
        "        self.res2[0,2] = nn.Linear(r, 1000)\n",
        "        self.res2[0,2].weight = nn.parameter.Parameter(Vr)\n",
        "        self.res2[0,2].bias = b\n",
        "        U, S, Vh = torch.linalg.svd(net.res2.layer2.weight)\n",
        "        b = the_model.res2.layer2.bias\n",
        "        Ur = U[:, :r]\n",
        "        Sr = S[:r,]\n",
        "        Vr = Vh.T[:, :r]\n",
        "        self.res2[1,0] = nn.Linear(1000, r, bias=False)\n",
        "        self.res2[1,0].weight = nn.parameter.Parameter(Ur.T)\n",
        "        self.res2[1,1] = nn.Linear(r, r, bias=False)\n",
        "        self.res2[1,1].weight = nn.parameter.Parameter(torch.diag(Sr))\n",
        "        self.res2[1,2] = nn.Linear(r, 1000)\n",
        "        self.res2[1,2].weight = nn.parameter.Parameter(Vr)\n",
        "        self.res2[1,2].bias = b \n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.net.fc1(x.view(-1, 3 * 32*32))\n",
        "        x = self.net.ReLU(x)\n",
        "        out = self.res1[0,0](x)\n",
        "        out = self.res1[0,1](out)\n",
        "        out = self.res1[0,2](out)\n",
        "        out = self.net.ReLU(out)\n",
        "        out = self.res1[1,0](out)\n",
        "        out = self.res1[1,1](out)\n",
        "        out = self.res1[1,2](out)\n",
        "        x = out + x\n",
        "        x = self.net.ReLU(x)\n",
        "        out = self.res2[0,0](x)\n",
        "        out = self.res2[0,1](out)\n",
        "        out = self.res2[0,2](out)\n",
        "        out = self.net.ReLU(out)\n",
        "        out = self.res2[1,0](out)\n",
        "        out = self.res2[1,1](out)\n",
        "        out = self.res2[1,2](out)\n",
        "        x = out + x\n",
        "        x = self.net.ReLU(x)\n",
        "        x = self.net.fc2(x)\n",
        "        return F.log_softmax(x, dim=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gQJlTNknxF_X",
        "outputId": "51a67898-43da-44ed-cf1c-0b468b979b34"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Epoch: 1 [0/50000 (0%)]\tLoss: 4.104497\n",
            "Train Epoch: 1 [5000/50000 (10%)]\tLoss: 1.778646\n",
            "Train Epoch: 1 [10000/50000 (20%)]\tLoss: 1.870077\n",
            "Train Epoch: 1 [15000/50000 (30%)]\tLoss: 1.449902\n",
            "Train Epoch: 1 [20000/50000 (40%)]\tLoss: 1.168819\n",
            "Train Epoch: 1 [25000/50000 (50%)]\tLoss: 1.534456\n",
            "Train Epoch: 1 [30000/50000 (60%)]\tLoss: 1.452371\n",
            "Train Epoch: 1 [35000/50000 (70%)]\tLoss: 1.219335\n",
            "Train Epoch: 1 [40000/50000 (80%)]\tLoss: 1.106096\n",
            "Train Epoch: 1 [45000/50000 (90%)]\tLoss: 1.826285\n",
            "\n",
            "Test set: Average loss: 1.7116, Accuracy: 5062/10000 (51%)\n",
            "\n",
            "Train Epoch: 2 [0/50000 (0%)]\tLoss: 1.061328\n",
            "Train Epoch: 2 [5000/50000 (10%)]\tLoss: 1.042471\n",
            "Train Epoch: 2 [10000/50000 (20%)]\tLoss: 1.006289\n",
            "Train Epoch: 2 [15000/50000 (30%)]\tLoss: 1.379351\n",
            "Train Epoch: 2 [20000/50000 (40%)]\tLoss: 0.989787\n",
            "Train Epoch: 2 [25000/50000 (50%)]\tLoss: 1.058346\n",
            "Train Epoch: 2 [30000/50000 (60%)]\tLoss: 0.728934\n",
            "Train Epoch: 2 [35000/50000 (70%)]\tLoss: 0.888622\n",
            "Train Epoch: 2 [40000/50000 (80%)]\tLoss: 1.133178\n",
            "Train Epoch: 2 [45000/50000 (90%)]\tLoss: 1.117829\n",
            "\n",
            "Test set: Average loss: 1.5805, Accuracy: 5163/10000 (52%)\n",
            "\n",
            "Train Epoch: 3 [0/50000 (0%)]\tLoss: 1.005871\n",
            "Train Epoch: 3 [5000/50000 (10%)]\tLoss: 0.735503\n",
            "Train Epoch: 3 [10000/50000 (20%)]\tLoss: 0.854880\n",
            "Train Epoch: 3 [15000/50000 (30%)]\tLoss: 0.957051\n",
            "Train Epoch: 3 [20000/50000 (40%)]\tLoss: 1.026931\n",
            "Train Epoch: 3 [25000/50000 (50%)]\tLoss: 1.033905\n",
            "Train Epoch: 3 [30000/50000 (60%)]\tLoss: 0.983458\n",
            "Train Epoch: 3 [35000/50000 (70%)]\tLoss: 1.015559\n",
            "Train Epoch: 3 [40000/50000 (80%)]\tLoss: 1.041101\n",
            "Train Epoch: 3 [45000/50000 (90%)]\tLoss: 1.018972\n",
            "\n",
            "Test set: Average loss: 1.5924, Accuracy: 5175/10000 (52%)\n",
            "\n",
            "Train Epoch: 4 [0/50000 (0%)]\tLoss: 0.861569\n",
            "Train Epoch: 4 [5000/50000 (10%)]\tLoss: 0.839715\n",
            "Train Epoch: 4 [10000/50000 (20%)]\tLoss: 0.933630\n",
            "Train Epoch: 4 [15000/50000 (30%)]\tLoss: 0.912841\n",
            "Train Epoch: 4 [20000/50000 (40%)]\tLoss: 0.899296\n",
            "Train Epoch: 4 [25000/50000 (50%)]\tLoss: 0.852590\n",
            "Train Epoch: 4 [30000/50000 (60%)]\tLoss: 0.864940\n",
            "Train Epoch: 4 [35000/50000 (70%)]\tLoss: 0.995917\n",
            "Train Epoch: 4 [40000/50000 (80%)]\tLoss: 0.980965\n",
            "Train Epoch: 4 [45000/50000 (90%)]\tLoss: 0.880519\n",
            "\n",
            "Test set: Average loss: 1.6264, Accuracy: 5163/10000 (52%)\n",
            "\n",
            "Train Epoch: 5 [0/50000 (0%)]\tLoss: 0.706142\n",
            "Train Epoch: 5 [5000/50000 (10%)]\tLoss: 0.862215\n",
            "Train Epoch: 5 [10000/50000 (20%)]\tLoss: 0.760551\n",
            "Train Epoch: 5 [15000/50000 (30%)]\tLoss: 0.700490\n",
            "Train Epoch: 5 [20000/50000 (40%)]\tLoss: 0.780328\n",
            "Train Epoch: 5 [25000/50000 (50%)]\tLoss: 0.752525\n",
            "Train Epoch: 5 [30000/50000 (60%)]\tLoss: 0.761722\n",
            "Train Epoch: 5 [35000/50000 (70%)]\tLoss: 0.638571\n",
            "Train Epoch: 5 [40000/50000 (80%)]\tLoss: 1.079649\n",
            "Train Epoch: 5 [45000/50000 (90%)]\tLoss: 0.950797\n",
            "\n",
            "Test set: Average loss: 1.6464, Accuracy: 5210/10000 (52%)\n",
            "\n",
            "Train Epoch: 6 [0/50000 (0%)]\tLoss: 0.742484\n",
            "Train Epoch: 6 [5000/50000 (10%)]\tLoss: 0.752273\n",
            "Train Epoch: 6 [10000/50000 (20%)]\tLoss: 0.674202\n",
            "Train Epoch: 6 [15000/50000 (30%)]\tLoss: 0.701993\n",
            "Train Epoch: 6 [20000/50000 (40%)]\tLoss: 0.834173\n",
            "Train Epoch: 6 [25000/50000 (50%)]\tLoss: 0.770057\n",
            "Train Epoch: 6 [30000/50000 (60%)]\tLoss: 0.808189\n",
            "Train Epoch: 6 [35000/50000 (70%)]\tLoss: 0.931911\n",
            "Train Epoch: 6 [40000/50000 (80%)]\tLoss: 0.739423\n",
            "Train Epoch: 6 [45000/50000 (90%)]\tLoss: 0.862138\n",
            "\n",
            "Test set: Average loss: 1.6891, Accuracy: 5247/10000 (52%)\n",
            "\n",
            "Train Epoch: 7 [0/50000 (0%)]\tLoss: 0.741732\n",
            "Train Epoch: 7 [5000/50000 (10%)]\tLoss: 0.791129\n",
            "Train Epoch: 7 [10000/50000 (20%)]\tLoss: 0.719366\n",
            "Train Epoch: 7 [15000/50000 (30%)]\tLoss: 0.854823\n",
            "Train Epoch: 7 [20000/50000 (40%)]\tLoss: 0.710971\n",
            "Train Epoch: 7 [25000/50000 (50%)]\tLoss: 0.682363\n",
            "Train Epoch: 7 [30000/50000 (60%)]\tLoss: 0.599620\n",
            "Train Epoch: 7 [35000/50000 (70%)]\tLoss: 0.571452\n",
            "Train Epoch: 7 [40000/50000 (80%)]\tLoss: 0.602120\n",
            "Train Epoch: 7 [45000/50000 (90%)]\tLoss: 0.950776\n",
            "\n",
            "Test set: Average loss: 1.7252, Accuracy: 5256/10000 (53%)\n",
            "\n",
            "CPU times: user 5min, sys: 2.39 s, total: 5min 3s\n",
            "Wall time: 5min 2s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "the_model =  Net()\n",
        "the_model.load_state_dict(state_dict)\n",
        "\n",
        "comp_model = CompNetR(the_model, 80)\n",
        "optimizer = optim.Adam(comp_model.parameters(), lr=1e-3)\n",
        "\n",
        "for epoch in range(1, epochs + 1):\n",
        "    train(comp_model,  train_loader, optimizer, epoch)\n",
        "    test(comp_model, test_loader)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pD1KPkrR6fdi"
      },
      "source": [
        "Okay, accuracy is a bit lower (53% vs 53% ~ 100 elements in difference) but we've saved more than a half of time (5 min vs 11 min)! That's a good result."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPGdLIT77iik"
      },
      "source": [
        "## Problem 4. «Reducio!» (15 pts)  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-TzgURdc7iik"
      },
      "source": [
        "Hermione Granger is well versed in all magical disciplines. In particular, she is a great expert in Numerical Linear Algebra and JAX.\n",
        "\n",
        "She has invited Harry Potter to play the game. \n",
        "\n",
        "Hermione chooses a number $r \\in [1, 95]$ and two matrices $W_1 \\in \\mathbb{R}^{r \\times 100}$ and $W_2 \\in \\mathbb{R}^{100 \\times r}$.\n",
        "Harry can tell her any 100-dimensional vector $x$, and Hermione gives Harry the result of \n",
        "\n",
        "$$ ||\\sin(W_2 \\cos(W_1 x))||^2_2,$$ \n",
        "\n",
        "where trigonometric functions are applied element-wise. The result is the squared norm of a 100-dimensional vector.\n",
        "\n",
        "Not to lose, Harry should guess what number $r$ Hermione has chosen.   \n",
        "Harry knows the python language, but he is an absolute layman in algebra. Please, help him to get at least 95% correct answers! \n",
        "\n",
        "<i> Hint 1: SVD might help you, but use it wisely! </i>\n",
        "\n",
        "<i> Hint 2: Suppose that a special magic allows you to compute gradients through automatic differentiation in JAX. You can also estimate gradients via finite differences, if you want it.</i>\n",
        "\n",
        "<i> Hint 3: You can estimate the matrix rank using simple heuristics.\n",
        "    \n",
        "You should write code into the `harry_answers` function. Good luck! "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 212,
      "metadata": {
        "id": "odWz6U6z7iil"
      },
      "outputs": [],
      "source": [
        "import jax.numpy as jnp\n",
        "import jax\n",
        "import numpy as np\n",
        "import random\n",
        "from jax import grad\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "models = []\n",
        "\n",
        "class Game:\n",
        "    def __init__(self, key):\n",
        "        # key is a jax.random.PRNGKey\n",
        "        self.key = key\n",
        "        return\n",
        "    def hermione_chooses_r_and_W(self):\n",
        "        self.r = random.randint(1, 95)\n",
        "        # print(self.r)\n",
        "        self.key, subkey = jax.random.split(self.key)\n",
        "        self.W1 = jax.random.uniform(subkey, (self.r, 100), maxval=100., minval=0., dtype=jnp.float32)\n",
        "        self.key, subkey = jax.random.split(self.key)\n",
        "        self.W2 = jax.random.uniform(subkey, (100, self.r), maxval=100., minval=0., dtype=jnp.float32)\n",
        "    def hermione_computes_function(self, x):\n",
        "        return jnp.sum(jnp.square(jnp.sin(self.W2 @ jnp.cos(self.W1 @ x))))\n",
        "    def harry_answers(self):\n",
        "        vectors = jax.numpy.eye(100)\n",
        "        gradients = []\n",
        "        for i in range(100):\n",
        "          gradients.append(grad(self.hermione_computes_function)(vectors[i,:]))\n",
        "        return np.sum((np.linalg.svd(np.asarray(gradients))[1] / np.linalg.svd(np.asarray(gradients))[1][0]) > 0.001)\n",
        "    \n",
        "    def play(self, n_rounds, verbose=True):\n",
        "        # n_rounds: a number of rounds of the game\n",
        "        # verbose: print or not the result of each round\n",
        "        n_right_answers = 0\n",
        "        for _ in range(n_rounds):\n",
        "            self.hermione_chooses_r_and_W()\n",
        "            r = self.harry_answers()\n",
        "            if abs(r - self.r) == 0:\n",
        "                if verbose:\n",
        "                    print(\"Good job! The true answer is {}, Harry's answer is {}!\".format(self.r, r))\n",
        "                n_right_answers += 1\n",
        "            else:\n",
        "                if verbose:\n",
        "                    print(\"Harry's answers is {}, but the true answer is {} :(\".format(r, self.r))\n",
        "        if float(n_right_answers) / n_rounds > 0.95:\n",
        "            print('Well done: {}/{} right answers!'.format(n_right_answers, n_rounds))\n",
        "        else:\n",
        "            print('Only {}/{} right answers :( Work a little more and you will succeed!'.format(\n",
        "                n_right_answers, n_rounds))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 214,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jW0JG_oF7iil",
        "outputId": "cf073d5d-8ebf-4293-9d10-8df5508e488b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Good job! The true answer is 29, Harry's answer is 29!\n",
            "Good job! The true answer is 38, Harry's answer is 38!\n",
            "Good job! The true answer is 52, Harry's answer is 52!\n",
            "Good job! The true answer is 78, Harry's answer is 78!\n",
            "Good job! The true answer is 67, Harry's answer is 67!\n",
            "Good job! The true answer is 84, Harry's answer is 84!\n",
            "Good job! The true answer is 65, Harry's answer is 65!\n",
            "Good job! The true answer is 74, Harry's answer is 74!\n",
            "Good job! The true answer is 15, Harry's answer is 15!\n",
            "Good job! The true answer is 53, Harry's answer is 53!\n",
            "Good job! The true answer is 73, Harry's answer is 73!\n",
            "Good job! The true answer is 42, Harry's answer is 42!\n",
            "Good job! The true answer is 79, Harry's answer is 79!\n",
            "Good job! The true answer is 3, Harry's answer is 3!\n",
            "Good job! The true answer is 13, Harry's answer is 13!\n",
            "Good job! The true answer is 52, Harry's answer is 52!\n",
            "Good job! The true answer is 4, Harry's answer is 4!\n",
            "Good job! The true answer is 76, Harry's answer is 76!\n",
            "Good job! The true answer is 71, Harry's answer is 71!\n",
            "Good job! The true answer is 21, Harry's answer is 21!\n",
            "Good job! The true answer is 71, Harry's answer is 71!\n",
            "Good job! The true answer is 83, Harry's answer is 83!\n",
            "Good job! The true answer is 37, Harry's answer is 37!\n",
            "Good job! The true answer is 35, Harry's answer is 35!\n",
            "Good job! The true answer is 21, Harry's answer is 21!\n",
            "Good job! The true answer is 20, Harry's answer is 20!\n",
            "Good job! The true answer is 39, Harry's answer is 39!\n",
            "Good job! The true answer is 60, Harry's answer is 60!\n",
            "Good job! The true answer is 45, Harry's answer is 45!\n",
            "Good job! The true answer is 8, Harry's answer is 8!\n",
            "Good job! The true answer is 62, Harry's answer is 62!\n",
            "Good job! The true answer is 32, Harry's answer is 32!\n",
            "Good job! The true answer is 25, Harry's answer is 25!\n",
            "Good job! The true answer is 21, Harry's answer is 21!\n",
            "Harry's answers is 91, but the true answer is 95 :(\n",
            "Good job! The true answer is 46, Harry's answer is 46!\n",
            "Good job! The true answer is 3, Harry's answer is 3!\n",
            "Good job! The true answer is 63, Harry's answer is 63!\n",
            "Good job! The true answer is 22, Harry's answer is 22!\n",
            "Harry's answers is 93, but the true answer is 95 :(\n",
            "Good job! The true answer is 19, Harry's answer is 19!\n",
            "Good job! The true answer is 63, Harry's answer is 63!\n",
            "Good job! The true answer is 40, Harry's answer is 40!\n",
            "Good job! The true answer is 79, Harry's answer is 79!\n",
            "Good job! The true answer is 58, Harry's answer is 58!\n",
            "Good job! The true answer is 3, Harry's answer is 3!\n",
            "Good job! The true answer is 68, Harry's answer is 68!\n",
            "Good job! The true answer is 8, Harry's answer is 8!\n",
            "Good job! The true answer is 14, Harry's answer is 14!\n",
            "Good job! The true answer is 75, Harry's answer is 75!\n",
            "Good job! The true answer is 59, Harry's answer is 59!\n",
            "Good job! The true answer is 60, Harry's answer is 60!\n",
            "Good job! The true answer is 65, Harry's answer is 65!\n",
            "Good job! The true answer is 50, Harry's answer is 50!\n",
            "Good job! The true answer is 29, Harry's answer is 29!\n",
            "Good job! The true answer is 79, Harry's answer is 79!\n",
            "Good job! The true answer is 41, Harry's answer is 41!\n",
            "Good job! The true answer is 86, Harry's answer is 86!\n",
            "Good job! The true answer is 71, Harry's answer is 71!\n",
            "Good job! The true answer is 10, Harry's answer is 10!\n",
            "Good job! The true answer is 71, Harry's answer is 71!\n",
            "Good job! The true answer is 45, Harry's answer is 45!\n",
            "Good job! The true answer is 11, Harry's answer is 11!\n",
            "Good job! The true answer is 54, Harry's answer is 54!\n",
            "Good job! The true answer is 45, Harry's answer is 45!\n",
            "Good job! The true answer is 52, Harry's answer is 52!\n",
            "Good job! The true answer is 18, Harry's answer is 18!\n",
            "Good job! The true answer is 11, Harry's answer is 11!\n",
            "Good job! The true answer is 51, Harry's answer is 51!\n",
            "Good job! The true answer is 80, Harry's answer is 80!\n",
            "Good job! The true answer is 27, Harry's answer is 27!\n",
            "Good job! The true answer is 58, Harry's answer is 58!\n",
            "Good job! The true answer is 18, Harry's answer is 18!\n",
            "Good job! The true answer is 52, Harry's answer is 52!\n",
            "Harry's answers is 92, but the true answer is 94 :(\n",
            "Good job! The true answer is 63, Harry's answer is 63!\n",
            "Good job! The true answer is 84, Harry's answer is 84!\n",
            "Good job! The true answer is 62, Harry's answer is 62!\n",
            "Good job! The true answer is 50, Harry's answer is 50!\n",
            "Good job! The true answer is 27, Harry's answer is 27!\n",
            "Good job! The true answer is 72, Harry's answer is 72!\n",
            "Good job! The true answer is 33, Harry's answer is 33!\n",
            "Good job! The true answer is 30, Harry's answer is 30!\n",
            "Harry's answers is 92, but the true answer is 95 :(\n",
            "Good job! The true answer is 37, Harry's answer is 37!\n",
            "Good job! The true answer is 66, Harry's answer is 66!\n",
            "Good job! The true answer is 70, Harry's answer is 70!\n",
            "Good job! The true answer is 69, Harry's answer is 69!\n",
            "Good job! The true answer is 79, Harry's answer is 79!\n",
            "Good job! The true answer is 44, Harry's answer is 44!\n",
            "Good job! The true answer is 49, Harry's answer is 49!\n",
            "Good job! The true answer is 67, Harry's answer is 67!\n",
            "Good job! The true answer is 64, Harry's answer is 64!\n",
            "Good job! The true answer is 38, Harry's answer is 38!\n",
            "Good job! The true answer is 25, Harry's answer is 25!\n",
            "Good job! The true answer is 40, Harry's answer is 40!\n",
            "Good job! The true answer is 73, Harry's answer is 73!\n",
            "Good job! The true answer is 8, Harry's answer is 8!\n",
            "Good job! The true answer is 13, Harry's answer is 13!\n",
            "Good job! The true answer is 33, Harry's answer is 33!\n",
            "Well done: 96/100 right answers!\n"
          ]
        }
      ],
      "source": [
        "key = jax.random.PRNGKey(713)\n",
        "game = Game(key)\n",
        "game.play(100, verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LCt0oMoatH4S"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
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      "collapsed_sections": [],
      "name": "hw1_(2) (4).ipynb",
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