partial_restore.ipynb

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   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
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    "## given a variable v0 of shape (h, w, c0),\n",
    "## we would like to create a new variable v1 of shape (h, w, c1)\n",
    "## such that (c1 > c0), and v1[:, :, :c0] == v0\n",
    "\n",
    "## here h = 5, w = 4, c0 = 3, c1 = 4\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "    v0 = tf.Variable(np.ones(shape=(5, 4, 3)))\n",
    "    v1 = tf.Variable(np.zeros(shape=(5, 4, 4)))\n",
    "    ## split v1 through the last axie\n",
    "    v1_splits = tf.split(value=v1, num_or_size_splits=4, axis=2)\n",
    "    ## replace first v1[:,:, :3] values with the copy of v0 values\n",
    "    assign_op = v1.assign(tf.concat([v0] + v1_splits[3:], axis=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
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     "text": [
      "v0: [[[ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]]\n",
      "\n",
      " [[ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]]\n",
      "\n",
      " [[ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]]\n",
      "\n",
      " [[ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]]\n",
      "\n",
      " [[ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]\n",
      "  [ 1.  1.  1.]]]\n",
      "\n",
      "v1 (before assignment): [[[ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]]\n",
      "\n",
      " [[ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]]\n",
      "\n",
      " [[ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]]\n",
      "\n",
      " [[ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]]\n",
      "\n",
      " [[ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]\n",
      "  [ 0.  0.  0.  0.]]]\n",
      "\n",
      "v1 (after assignment): [[[ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]]\n",
      "\n",
      " [[ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]]\n",
      "\n",
      " [[ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]]\n",
      "\n",
      " [[ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]]\n",
      "\n",
      " [[ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]\n",
      "  [ 1.  1.  1.  0.]]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with tf.Session(graph=graph) as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    graph.finalize()\n",
    "    print(\"v0: %s\\n\" % sess.run(v0))\n",
    "    print(\"v1 (before assignment): %s\\n\" % sess.run(v1))\n",
    "    sess.run(assign_op)\n",
    "    print(\"v1 (after assignment): %s\\n\" % sess.run(v1))"
   ]
  }
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