{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"LSTM_REG_EMB.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1Ai3ZTOS4Pft0o0KiCkQRCMuYdg27phr_","authorship_tag":"ABX9TyMmXwJYSrt9dsExhM+NinF5"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"5zn9maoCzLyT","colab_type":"code","outputId":"7a7c7bc2-02b8-4951-c738-6769535b5e2c","executionInfo":{"status":"ok","timestamp":1582139778045,"user_tz":0,"elapsed":4256,"user":{"displayName":"Andy Wang","photoUrl":"","userId":"02776860930356410397"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["import torch\n","import torch.nn as nn\n","import numpy as np\n","from torchvision import datasets, transforms\n","\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","GPU = True\n","device_idx = 0\n","if GPU:\n"," device = torch.device(\"cuda:\" + str(device_idx) if torch.cuda.is_available() else \"cpu\")\n","else:\n"," device = torch.device(\"cpu\")\n","print(device)\n","\n","# Set default dtype for model weights\n","torch.set_default_dtype(torch.double)"],"execution_count":1,"outputs":[{"output_type":"stream","text":["cuda:0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Lhv5MZ3JyH59","colab_type":"code","colab":{}},"source":["# Load the data\n","import os\n","os.chdir(\"/content/drive/My Drive/Colab Notebooks/NLP/coursework\")\n","import pickle\n","with open(\"en_indices.pk\", \"rb\") as f:\n"," en_sentences_vectors = pickle.load(f)\n","with open(\"de_indices.pk\", \"rb\") as f:\n"," de_sentences_vectors = pickle.load(f)\n","with open(\"scores.pk\", \"rb\") as f:\n"," scores = pickle.load(f)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"JTm0ONuPB7Z2","colab_type":"code","outputId":"fb608a1d-3655-4352-c4b9-9f762c0c879e","executionInfo":{"status":"ok","timestamp":1582139939789,"user_tz":0,"elapsed":1369,"user":{"displayName":"Andy Wang","photoUrl":"","userId":"02776860930356410397"}},"colab":{"base_uri":"https://localhost:8080/","height":545}},"source":["en_lengths = np.array([len(en_sentences_vectors[i]) for i in range(len(en_sentences_vectors))])\n","de_lengths = np.array([len(de_sentences_vectors[i]) for i in range(len(de_sentences_vectors))])\n","scores = np.array(scores)\n","\n","# Plotting the lengths of sentences to see where we should pad\n","sns.distplot(en_lengths)\n","plt.title(\"Lengths of sentences EN\")\n","plt.show()\n","sns.distplot(de_lengths)\n","plt.title(\"Lengths of sentences DE\")\n","plt.show()\n"],"execution_count":6,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3deXxV9Z3/8dfn3mxAQtjCFggJiwJu\nKJuKe9FiW6W2Lrh03CrVyrQzWh27/Kpj2+k2ddpO1eK4tTrutjVWrHUDV5CgLAIiCWvCFiDskJDk\n8/vjHjrXGMyFLPfm5P18PO4jZz+fe7i8c/I933OuuTsiIhJekWQXICIirUtBLyIScgp6EZGQU9CL\niIScgl5EJOQU9CIiIaegl5RiZqvMbGIr76OPmb1hZjvN7FetuS+RVKCgF6BtAraRfT5sZj9uy30G\npgKbga7ufnNb7NDMzjCz8rbYV0sws6vMrM7MdjV49Q/mrzKzTWbWJW6dr5vZzKQVLQeloJeOaBCw\nxHW3YFPedffsBq91cfOjwLeTVZwkTkEvTTKzL5nZfDPbZmbvmNmxcfNWmdl3zGyhmW03syfNLCtu\n/q1mtt7M1gVnfG5mQ81sKnA5cGtwpvh83C5HNbY9M+tlZn8N6thqZm+aWaOfYTM72czmBtuYa2Yn\nB9MfBq6M2++n/ooxsy+Y2ZKgaafCzL7TnGMRnPW+CPSPPzM2s4iZ3WZmZWa2xcyeMrMewbYKg2N1\npZmtMbPNZvb9uH1Fzex7wbo7zWyemQ0M5g03s5eDY7TMzC5O5L0dhl8C3zGzbs3YhrQFd9dLL4BV\nwMRGph8PbALGEzuDuzJYNjNuvfeA/kAPYClwfTBvErABOAroDDwKODA0mP8w8ONG6jjY9n4K/B5I\nD16nAtZIzT2AKuBrQBpwaTDe82D7bbD+euDUYLg7cEILHIszgPIG+/k2MBsYAGQC04HHg3mFwbH6\nH6ATcBxQDYwI5t8CLAKOBCyY3xPoAqwFrg7e+/HEmqlGftZ7a+QYXAW81dTnBfjTgWMJfB2YmezP\nsl6ffumMXpoyFZju7nPcvc7d/0AscE6MW+a37r7O3bcCzwOjgukXAw+5+2J33wPckeA+D7a9/UA/\nYJC773f3Nz1ImAa+CCx390fcvdbdHwc+As5LcP/7gZFm1tXdq9z9/WB6c45FY64Hvu/u5e5eTez4\nXGhmaXHL/Lu773X3BcACYoEOsVD9gbsv85gF7r4F+BKwyt0fCt77B8CzwEVNvLfGnBj85XLgVdbI\nMj8E/tnM8j5jO5JkCnppyiDg5vj/8MBAYmetB2yIG94DZAfD/YmdXR4QP/xZDra9XwKlwN/NbIWZ\n3XaQ9fsDqxtMWw3kJ7j/rwJfAFab2SwzOymY3pxj0ZhBwJ/jtrUUqAP6JLC9gUBjwTsIGN+gxsuB\nvk28t8bMdvduca8hDRdw9w+BvwIH+7eQFKCgl6asBX7S4D985+AsuSnriTVLHDCwwfxDuhjq7jvd\n/WZ3HwycD9xkZp9rZNF1xAIvXgFQkeB+5rr7ZKA38BfgqWBWc45FY+91LXBug+1luXsida4FPhW8\nwfRZDbaZ7e43NPHemuN24DoS/0UqbUxBL/HSg4uHB15pxNqIrzez8RbTxcy+aGY5CWzvKeBqMxth\nZp2B/9dg/kZgcKLFBRdCh5qZAduJnf3WN7LoDOAIM7vMzNLM7BJgJLEzz6b2kWFml5tZrrvvB3bE\n7aM5x2Ij0NPMcuOm/R74iZkNCvadZ2aTE9gWwP3Aj8xsWFDLsWbWM3iPR5jZ18wsPXiNDf4NPuu9\nHTZ3LwWeBL7V3G1J61DQS7wZwN641x3uXkLsbO13xC5olhK7UNckd38R+C3werDe7GBWdfDzAWLt\nxdvM7C8JbHIY8AqwC3gXuMfdX29kvwfaqm8GtgC3Al9y982J1E3sIu4qM9tBrB398mC7zTkWHwGP\nAyuC99sf+A1QTKwpaiex4zM+wRrvIvaL9O/EAvsBoJO77wTOAaYQ+8tmA/BzYhd7D/reDuIk+3Q/\n+rEHWfZOYheCJQVZ49eyRFqemY0APiTWS6U22fWIdBQ6o5dWZWYXmFmmmXUndmb5vEJepG0p6KW1\nfYNY3/MyYm3qNyS3HJGOR003IiIhpzN6EZGQS2t6kbbVq1cvLywsTHYZIiLtyrx58za7e6N3KKdc\n0BcWFlJSUpLsMkRE2hUza3g3+D+o6UZEJOQU9CIiIaegFxEJOQW9iEjIKehFREJOQS8iEnIKehGR\nkFPQi4iEnIJeRCTkUu7OWEl9j81Z0+j0y8YXtHElIpIIndGLiISczug7GJ2Ni3Q8OqMXEQk5Bb2I\nSMgp6EVEQk5BLyIScgp6EZGQU9CLiIScgl5EJOQU9CIiIaegFxEJOQW9iEjIKehFREJOQS8iEnIJ\nBb2ZTTKzZWZWama3NTL/ejNbZGbzzewtMxsZN++7wXrLzOzzLVm8iIg0rcmgN7MocDdwLjASuDQ+\nyAOPufsx7j4K+AVwV7DuSGAKcBQwCbgn2J6IiLSRRM7oxwGl7r7C3WuAJ4DJ8Qu4+4640S6AB8OT\ngSfcvdrdVwKlwfZERKSNJPI8+nxgbdx4OTC+4UJmdiNwE5ABnBW37uwG6+Y3su5UYCpAQYGeiy4i\n0pJa7GKsu9/t7kOAfwN+cIjr3ufuY9x9TF5eXkuVJCIiJBb0FcDAuPEBwbSDeQL48mGuKyIiLSyR\noJ8LDDOzIjPLIHZxtTh+ATMbFjf6RWB5MFwMTDGzTDMrAoYB7zW/bBERSVSTbfTuXmtm04CXgCjw\noLsvNrM7gRJ3LwammdlEYD9QBVwZrLvYzJ4ClgC1wI3uXtdK70VERBqR0JeDu/sMYEaDaT+MG/72\nZ6z7E+Anh1ugiIg0j+6MFREJOQW9iEjIKehFREJOQS8iEnIKehGRkEuo142ktsfmrGl0+mXjU+Nx\nEo3Vlyq1iXQEOqMXEQk5Bb2ISMgp6EVEQk5BLyIScgp6EZGQU9CLiIScgl5EJOQU9CIiIaegFxEJ\nOd0ZKykl1e/yFWmPdEYvIhJyCnoRkZBT0IuIhJyCXkQk5BT0IiIhp6AXEQk5Bb2ISMglFPRmNsnM\nlplZqZnd1sj8m8xsiZktNLNXzWxQ3Lw6M5sfvIpbsngREWlakzdMmVkUuBs4GygH5ppZsbsviVvs\nA2CMu+8xsxuAXwCXBPP2uvuoFq5bREQSlMgZ/Tig1N1XuHsN8AQwOX4Bd3/d3fcEo7OBAS1bpoiI\nHK5Egj4fWBs3Xh5MO5hrgRfjxrPMrMTMZpvZlxtbwcymBsuUVFZWJlCSiIgkqkWfdWNmVwBjgNPj\nJg9y9wozGwy8ZmaL3L0sfj13vw+4D2DMmDHekjWJiHR0iZzRVwAD48YHBNM+wcwmAt8Hznf36gPT\n3b0i+LkCmAkc34x6RUTkECUS9HOBYWZWZGYZwBTgE71nzOx4YDqxkN8UN727mWUGw72ACUD8RVwR\nEWllTTbduHutmU0DXgKiwIPuvtjM7gRK3L0Y+CWQDTxtZgBr3P18YAQw3czqif1S+VmD3joiItLK\nEmqjd/cZwIwG034YNzzxIOu9AxzTnAJFRKR5dGesiEjIKehFREJOQS8iEnIKehGRkFPQi4iEnIJe\nRCTkFPQiIiGnoBcRCTkFvYhIyCnoRURCTkEvIhJyCnoRkZBT0IuIhJyCXkQk5BT0IiIhp6AXEQk5\nBb2ISMgp6EVEQk5BLyIScgp6EZGQU9CLiIScgl5EJOQSCnozm2Rmy8ys1Mxua2T+TWa2xMwWmtmr\nZjYobt6VZrY8eF3ZksWLiEjT0ppawMyiwN3A2UA5MNfMit19SdxiHwBj3H2Pmd0A/AK4xMx6ALcD\nYwAH5gXrVrX0GxE54LE5axqdftn4gjauRCQ1JHJGPw4odfcV7l4DPAFMjl/A3V939z3B6GxgQDD8\neeBld98ahPvLwKSWKV1ERBKRSNDnA2vjxsuDaQdzLfDioaxrZlPNrMTMSiorKxMoSUREEtWiF2PN\n7ApizTS/PJT13P0+dx/j7mPy8vJasiQRkQ4vkaCvAAbGjQ8Ipn2CmU0Evg+c7+7Vh7KuiIi0nkSC\nfi4wzMyKzCwDmAIUxy9gZscD04mF/Ka4WS8B55hZdzPrDpwTTBMRkTbSZK8bd681s2nEAjoKPOju\ni83sTqDE3YuJNdVkA0+bGcAadz/f3bea2Y+I/bIAuNPdt7bKOxERkUY1GfQA7j4DmNFg2g/jhid+\nxroPAg8eboEiItI8ujNWRCTkFPQiIiGnoBcRCTkFvYhIyCnoRURCTkEvIhJyCnoRkZBT0IuIhJyC\nXkQk5BK6M1YkzBr7ohJ9SYmEic7oRURCTkEvIhJyCnoRkZBT0IuIhJyCXkQk5BT0IiIhp6AXEQk5\nBb2ISMjphqkka+xmHdANOyLScnRGLyIScgp6EZGQU9CLiIRcQkFvZpPMbJmZlZrZbY3MP83M3jez\nWjO7sMG8OjObH7yKW6pwERFJTJMXY80sCtwNnA2UA3PNrNjdl8Qttga4CvhOI5vY6+6jWqBWERE5\nDIn0uhkHlLr7CgAzewKYDPwj6N19VTCvvhVqFBGRZkik6SYfWBs3Xh5MS1SWmZWY2Wwz+/IhVSci\nIs3WFv3oB7l7hZkNBl4zs0XuXha/gJlNBaYCFBSo/7iISEtK5Iy+AhgYNz4gmJYQd68Ifq4AZgLH\nN7LMfe4+xt3H5OXlJbppERFJQCJBPxcYZmZFZpYBTAES6j1jZt3NLDMY7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XMajDu9qjh/cDURpb7G/C3I6yx3ftw/XZ27a/lW18oiHcp0gbSUpK4dGx/rjgxn/94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\noY4AAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"r-pmaN-XE-4o","colab_type":"code","outputId":"8138fed1-312a-47f8-90b6-8bad1c44a74d","executionInfo":{"status":"ok","timestamp":1582139943940,"user_tz":0,"elapsed":1537,"user":{"displayName":"Andy Wang","photoUrl":"","userId":"02776860930356410397"}},"colab":{"base_uri":"https://localhost:8080/","height":809}},"source":["# Split into 2 group so that inputs are not too sparse\n","# Check whether the proposed splits have the same distribution of scores\n","sns.distplot(scores)\n","plt.title(\"Full Distribution of scores\")\n","plt.show()\n","\n","sns.distplot(scores[en_lengths < 17])\n","plt.title(\"EN_SENT, len < 17\")\n","plt.show()\n","\n","sns.distplot(scores[en_lengths >= 17])\n","plt.title(\"EN_SENT, len >= 17\")\n","plt.show()\n","\n","# sns.distplot(scores[de_lengths < 17])\n","# plt.title(\"DE_SENT, len < 17\")\n","# plt.show()\n","\n","# sns.distplot(scores[de_lengths >= 17])\n","# plt.title(\"DE_SENT, len >= 17\")\n","# plt.show()"],"execution_count":7,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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P5Eead3BzjA5esY2zceayxnccbO/jekf1UFIXYUreIC9dVz/v5+Qs20DN5NZhkt3j/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\nyRIKdBGRLPH/ASdZhBlG4tYVAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"K5q6FF8aXXfr","colab_type":"code","colab":{}},"source":["# Split up into two seperate sets and pad\n","pad = [0]\n","\n","scores_set_1 = scores[en_lengths < 17]\n","scores_set_2 = scores[en_lengths >= 17]\n","\n","en_set_1 = []\n","en_set_2 = []\n","idx_set_1 = []\n","idx_set_2 = []\n","\n","max_len_en = max(len(sent) for sent in en_sentences_vectors)\n","\n","for i, en_sentence in enumerate(en_sentences_vectors):\n"," sent_len = len(en_sentence)\n"," if sent_len < 17:\n"," en_set_1.append(en_sentence + pad * (16 - sent_len))\n"," idx_set_1.append(i)\n"," else:\n"," en_set_2.append(en_sentence + pad * (max_len_en - sent_len))\n"," idx_set_2.append(i)\n","\n","\n","de_set_1 = []\n","de_set_2 = []\n","max_len_de_set_1 = max(de_lengths[idx_set_1])\n","max_len_de_set_2 = max(len(sent) for sent in de_sentences_vectors)\n","\n","for idx in idx_set_1:\n"," de_set_1.append(de_sentences_vectors[idx] + pad * (max_len_de_set_1 - len(de_sentences_vectors[idx])))\n","for idx in idx_set_2:\n"," de_set_2.append(de_sentences_vectors[idx] + pad * (max_len_de_set_2 - len(de_sentences_vectors[idx])))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"q43POV9QytX_","colab_type":"code","colab":{}},"source":["class Sentences(torch.utils.data.Dataset):\n"," def __init__(self, en_sentences, de_sentences, scores):\n"," super(Sentences, self).__init__()\n"," self.en_sentences_vectors = torch.tensor(en_sentences, device=device)\n"," self.de_sentences_vectors = torch.tensor(de_sentences, device=device)\n"," self.scores = torch.tensor(scores, device=device)\n","\n"," def __len__(self):\n"," return len(self.scores)\n","\n"," def __getitem__(self, idx):\n"," if torch.is_tensor(idx):\n"," idx = idx.tolist()\n"," \n"," en, de, scores = self.en_sentences_vectors[idx], self.de_sentences_vectors[idx], self.scores[idx]\n"," return en, de, scores"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"KOd75PS_fGoA","colab_type":"code","colab":{}},"source":["# Create train and test sets\n","train_pct = 0.8\n","first_set_train_len = int(train_pct * len(en_set_1))\n","second_set_train_len = int(train_pct * len(en_set_2))\n","\n","first_set = Sentences(en_set_1, de_set_1, scores_set_1)\n","second_set = Sentences(en_set_2, de_set_2, scores_set_2)\n","\n","first_set_train, first_set_test = torch.utils.data.random_split(first_set, [first_set_train_len, len(en_set_1) - first_set_train_len])\n","second_set_train, second_set_test = torch.utils.data.random_split(second_set, [second_set_train_len, len(en_set_2) - second_set_train_len])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"fkw5SZx1fTZZ","colab_type":"code","colab":{}},"source":["# Create Dataloaders\n","batch_size = 32\n","\n","loader_first_train = torch.utils.data.DataLoader(first_set_train, batch_size=batch_size, shuffle=True)\n","loader_first_test = torch.utils.data.DataLoader(first_set_test, batch_size=batch_size, shuffle=False)\n","\n","loader_second_train = torch.utils.data.DataLoader(second_set_train, batch_size=batch_size, shuffle=True)\n","loader_second_test = torch.utils.data.DataLoader(second_set_test, batch_size=batch_size, shuffle=False)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"O7keA4QQ0x0t","colab_type":"code","colab":{}},"source":["# https://stackoverflow.com/questions/53010465/bidirectional-lstm-output-question-in-pytorch\n","# Hyper Parameters\n","en_vocab_size = 25237\n","de_vocab_size = 27854\n","embed_dim = 300\n","lstm_hidden_dim = 256\n","num_layers = 2\n","reg_inputs = 4 * lstm_hidden_dim\n","reg_hid_dim1 = 2 * lstm_hidden_dim\n","reg_hid_dim2 = lstm_hidden_dim\n","learning_rate = 1e-4\n","\n","class LSTM_REG(torch.nn.Module):\n"," def __init__(self):\n"," super(LSTM_REG, self).__init__()\n"," self.en_emb = torch.nn.Embedding(en_vocab_size, embed_dim, padding_idx=0)\n"," self.de_emb = torch.nn.Embedding(de_vocab_size, embed_dim, padding_idx=0)\n"," self.en_lstm = torch.nn.GRU(embed_dim, lstm_hidden_dim, num_layers, bidirectional=True, batch_first=True)\n"," self.de_lstm = torch.nn.GRU(embed_dim, lstm_hidden_dim, num_layers, bidirectional=True, batch_first=True)\n"," self.reg = torch.nn.Sequential(\n"," nn.Dropout(),\n"," nn.Linear(reg_inputs, reg_hid_dim1),\n"," nn.ReLU(),\n"," nn.Linear(reg_hid_dim1, reg_hid_dim2),\n"," nn.ReLU(),\n"," nn.Linear(reg_hid_dim2, 1)\n"," )\n","\n"," def forward(self, en_batch, de_batch):\n"," # inputs should be 3D, BATCH, NUM_WORDS, lstm_hidden_dim\n"," en_emb = self.en_emb(en_batch)\n"," de_emb = self.de_emb(de_batch)\n"," en_all_hids, en_last_hid = self.en_lstm(en_emb)\n"," de_all_hids, de_last_hid = self.de_lstm(de_emb)\n","\n"," # using last state of last layer in each direction\n"," en_last_hid_resh = en_last_hid.view(num_layers, 2, en_batch.size(0), lstm_hidden_dim)\n"," en_reg_input_1 = en_last_hid_resh[-1, 0, :, :]\n"," en_reg_input_2 = en_last_hid_resh[-1, 1, :, :]\n","\n"," de_last_hid_resh = de_last_hid.view(num_layers, 2, de_batch.size(0), lstm_hidden_dim)\n"," de_reg_input_1 = de_last_hid_resh[-1, 0, :, :]\n"," de_reg_input_2 = de_last_hid_resh[-1, 1, :, :]\n","\n"," reg_input = torch.cat((en_reg_input_1, en_reg_input_2, de_reg_input_1, de_reg_input_2), dim=-1)\n","\n"," out = self.reg(reg_input)\n"," return out\n","\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tFgnl4bonSGg","colab_type":"code","colab":{}},"source":["# Create model\n","model = LSTM_REG()\n","model = model.to(device)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uDNWHcEh0lxP","colab_type":"code","colab":{}},"source":["# Optimiser\n","optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"M3IepQDap5AW","colab_type":"code","colab":{}},"source":["# Loss function\n","def calc_loss(output, target):\n"," loss = torch.nn.functional.mse_loss(output.squeeze(), target, reduction=\"mean\")\n"," return loss\n","\n","# Test function\n","def eval_target(output, target):\n"," acc = torch.nn.functional.l1_loss(output.squeeze(), target, reduction=\"mean\")\n"," return acc"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VieQzJimED7Y","colab_type":"code","outputId":"4cbde0f8-cb80-4e37-f048-0344ea756542","executionInfo":{"status":"ok","timestamp":1582141354937,"user_tz":0,"elapsed":552804,"user":{"displayName":"Andy Wang","photoUrl":"","userId":"02776860930356410397"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["num_epochs = 100\n","\n","verbose_print = False\n","\n","train_losses = []\n","test_losses = []\n","test_acc = []\n","test_pearson = []\n","\n","for epoch in range(num_epochs):\n"," model.train()\n"," epoch_loss = []\n"," total_steps = len(loader_first_train) + len(loader_second_train)\n"," for idx_1, (en, de, labels) in enumerate(loader_first_train):\n"," model.zero_grad()\n"," en = en.to(device)\n"," de = de.to(device)\n"," labels = labels.to(device)\n"," \n"," output = model(en, de)\n"," loss = calc_loss(output, labels)\n"," loss.backward()\n"," avg_batch_loss = loss.item()\n"," optimizer.step()\n"," epoch_loss.append(avg_batch_loss)\n"," if verbose_print:\n"," print(f\"Epoch {epoch}, Batch {idx_1 + 1} Train Loss: {avg_batch_loss:.4f}\")\n","\n"," for idx_2, (en, de, labels) in enumerate(loader_second_train):\n"," model.zero_grad()\n"," en = en.to(device)\n"," de = de.to(device)\n"," labels = labels.to(device)\n"," \n"," output = model(en, de)\n"," loss = calc_loss(output, labels)\n"," loss.backward()\n"," avg_batch_loss = loss.item()\n"," optimizer.step()\n"," epoch_loss.append(avg_batch_loss)\n"," if verbose_print:\n"," print(f\"Epoch {epoch}, Batch {idx_1 + idx_2 + 2} Train Loss: {avg_batch_loss:.4f}\")\n"," \n"," avg_epoch_loss = sum(epoch_loss) / total_steps\n"," train_losses.append(avg_epoch_loss)\n"," print(f\"Average Train Loss in Epoch {epoch}: {avg_epoch_loss:.4f}\")\n","\n"," # Test on test set\n","\n"," model.eval()\n"," epoch_loss = []\n"," epoch_acc = []\n","\n"," all_outputs = []\n"," all_labels = []\n","\n"," total_steps = len(loader_first_test) + len(loader_second_test)\n"," with torch.no_grad():\n"," for idx_1, (en, de, labels) in enumerate(loader_first_test):\n"," # Record for Pearson\n"," all_labels.extend(labels.tolist())\n","\n"," en = en.to(device)\n"," de = de.to(device)\n"," labels = labels.to(device)\n"," \n"," output = model(en, de)\n"," # Record for Pearson\n"," all_outputs.extend(output.squeeze().tolist())\n","\n"," loss = calc_loss(output, labels)\n"," avg_batch_loss = loss.item()\n"," acc = eval_target(output, labels)\n"," avg_batch_acc= acc.item()\n"," epoch_loss.append(avg_batch_loss)\n"," epoch_acc.append(avg_batch_acc)\n"," if verbose_print:\n"," print(f\"Epoch {epoch}, Batch {idx_1 + 1} Test Loss: {avg_batch_loss:.4f}, Test Acc: {avg_batch_acc:.4f}\")\n","\n"," for idx_2, (en, de, labels) in enumerate(loader_second_test):\n"," en = en.to(device)\n"," de = de.to(device)\n"," labels = labels.to(device)\n"," \n"," output = model(en, de)\n"," loss = calc_loss(output, labels)\n"," avg_batch_loss = loss.item()\n"," acc = eval_target(output, labels)\n"," avg_batch_acc= acc.item()\n"," epoch_loss.append(avg_batch_loss)\n"," epoch_acc.append(avg_batch_acc)\n"," if verbose_print:\n"," print(f\"Epoch {epoch}, Batch {idx_1 + idx_2 + 2} Test Loss: {avg_batch_loss:.4f}, Test Acc: {avg_batch_acc:.4f}\")\n"," \n"," avg_epoch_loss = sum(epoch_loss) / total_steps\n"," avg_epoch_acc = sum(epoch_acc) / total_steps\n"," test_losses.append(avg_epoch_loss)\n"," test_acc.append(avg_epoch_acc)\n"," print(f\"Average Test Loss in Epoch {epoch}: {avg_epoch_loss:.4f}\")\n"," print(f\"Average Test Acc in Epoch {epoch}: {avg_epoch_acc:.4f}\")\n","\n"," # Calc Pearson\n"," pearson = np.corrcoef(all_outputs, all_labels)[0, 1]\n"," test_pearson.append(pearson)\n"," print(f\"Pearson Coeff in Epoch {epoch}: {pearson}\")"],"execution_count":19,"outputs":[{"output_type":"stream","text":["Average Train Loss in Epoch 0: 0.0064\n","Average Test Loss in Epoch 0: 0.7037\n","Average Test Acc in Epoch 0: 0.5604\n","Pearson Coeff in Epoch 0: -0.00640668765047935\n","Average Train Loss in Epoch 1: 0.0070\n","Average Test Loss in Epoch 1: 0.7356\n","Average Test Acc in Epoch 1: 0.5793\n","Pearson Coeff in Epoch 1: -0.006979045487867788\n","Average Train Loss in Epoch 2: 0.0070\n","Average Test Loss in Epoch 2: 0.7183\n","Average Test Acc in Epoch 2: 0.5663\n","Pearson Coeff in Epoch 2: -0.008282282412511641\n","Average Train Loss in Epoch 3: 0.0070\n","Average Test Loss in Epoch 3: 0.7177\n","Average Test Acc in Epoch 3: 0.5695\n","Pearson Coeff in Epoch 3: -0.0060183350042013065\n","Average Train Loss in Epoch 4: 0.0069\n","Average Test Loss in Epoch 4: 0.7513\n","Average Test Acc in Epoch 4: 0.5851\n","Pearson Coeff in Epoch 4: -0.01662392594265492\n","Average Train Loss in Epoch 5: 0.0084\n","Average Test Loss in Epoch 5: 0.7393\n","Average Test Acc in Epoch 5: 0.5792\n","Pearson Coeff in Epoch 5: -0.010780362453402855\n","Average Train Loss in Epoch 6: 0.0062\n","Average Test Loss in Epoch 6: 0.7309\n","Average Test Acc in Epoch 6: 0.5760\n","Pearson Coeff in Epoch 6: -0.007079517514670253\n","Average Train Loss in Epoch 7: 0.0061\n","Average Test Loss in Epoch 7: 0.7319\n","Average Test Acc in Epoch 7: 0.5740\n","Pearson Coeff in Epoch 7: -0.013774120205408132\n","Average Train Loss in Epoch 8: 0.0065\n","Average Test Loss in Epoch 8: 0.7202\n","Average Test Acc in Epoch 8: 0.5689\n","Pearson Coeff in Epoch 8: -0.012118685158348044\n","Average Train Loss in Epoch 9: 0.0066\n","Average Test Loss in Epoch 9: 0.7192\n","Average Test Acc in Epoch 9: 0.5707\n","Pearson Coeff in Epoch 9: -0.0071289457160448495\n","Average Train Loss in Epoch 10: 0.0069\n","Average Test Loss in Epoch 10: 0.7193\n","Average Test Acc in Epoch 10: 0.5656\n","Pearson Coeff in Epoch 10: -0.011515248369711595\n","Average Train Loss in Epoch 11: 0.0060\n","Average Test Loss in Epoch 11: 0.7236\n","Average Test Acc in Epoch 11: 0.5703\n","Pearson Coeff in Epoch 11: -0.005069177373407349\n","Average Train Loss in Epoch 12: 0.0062\n","Average Test Loss in Epoch 12: 0.7340\n","Average Test Acc in Epoch 12: 0.5758\n","Pearson Coeff in Epoch 12: -0.011562655037911231\n","Average Train Loss in Epoch 13: 0.0079\n","Average Test Loss in Epoch 13: 0.7214\n","Average Test Acc in Epoch 13: 0.5684\n","Pearson Coeff in Epoch 13: -0.008449650566705465\n","Average Train Loss in Epoch 14: 0.0072\n","Average Test Loss in Epoch 14: 0.7141\n","Average Test Acc in Epoch 14: 0.5676\n","Pearson Coeff in Epoch 14: -0.006097211910686325\n","Average Train Loss in Epoch 15: 0.0059\n","Average Test Loss in Epoch 15: 0.7171\n","Average Test Acc in Epoch 15: 0.5663\n","Pearson Coeff in Epoch 15: -0.00868391500578421\n","Average Train Loss in Epoch 16: 0.0063\n","Average Test Loss in Epoch 16: 0.7132\n","Average Test Acc in Epoch 16: 0.5626\n","Pearson Coeff in Epoch 16: -0.0068288151125823265\n","Average Train Loss in Epoch 17: 0.0073\n","Average Test Loss in Epoch 17: 0.7285\n","Average Test Acc in Epoch 17: 0.5689\n","Pearson Coeff in Epoch 17: -0.015800308472356018\n","Average Train Loss in Epoch 18: 0.0079\n","Average Test Loss in Epoch 18: 0.7174\n","Average Test Acc in Epoch 18: 0.5664\n","Pearson Coeff in Epoch 18: -0.006678423310916171\n","Average Train Loss in Epoch 19: 0.0070\n","Average Test Loss in Epoch 19: 0.7112\n","Average Test Acc in Epoch 19: 0.5584\n","Pearson Coeff in Epoch 19: -0.012301081725153455\n","Average Train Loss in Epoch 20: 0.0066\n","Average Test Loss in Epoch 20: 0.7375\n","Average Test Acc in Epoch 20: 0.5748\n","Pearson Coeff in Epoch 20: -0.011193149754333446\n","Average Train Loss in Epoch 21: 0.0058\n","Average Test Loss in Epoch 21: 0.7168\n","Average Test Acc in Epoch 21: 0.5616\n","Pearson Coeff in Epoch 21: -0.016230629153410902\n","Average Train Loss in Epoch 22: 0.0061\n","Average Test Loss in Epoch 22: 0.7361\n","Average Test Acc in Epoch 22: 0.5745\n","Pearson Coeff in Epoch 22: -0.008928321861264556\n","Average Train Loss in Epoch 23: 0.0061\n","Average Test Loss in Epoch 23: 0.7311\n","Average Test Acc in Epoch 23: 0.5724\n","Pearson Coeff in Epoch 23: -0.010055173007708168\n","Average Train Loss in Epoch 24: 0.0055\n","Average Test Loss in Epoch 24: 0.7179\n","Average Test Acc in Epoch 24: 0.5661\n","Pearson Coeff in Epoch 24: -0.010154973152838248\n","Average Train Loss in Epoch 25: 0.0058\n","Average Test Loss in Epoch 25: 0.7151\n","Average Test Acc in Epoch 25: 0.5665\n","Pearson Coeff in Epoch 25: -0.014390283954534358\n","Average Train Loss in Epoch 26: 0.0065\n","Average Test Loss in Epoch 26: 0.6966\n","Average Test Acc in Epoch 26: 0.5547\n","Pearson Coeff in Epoch 26: -0.006887657576059487\n","Average Train Loss in Epoch 27: 0.0074\n","Average Test Loss in Epoch 27: 0.7502\n","Average Test Acc in Epoch 27: 0.5811\n","Pearson Coeff in Epoch 27: -0.013991437861377506\n","Average Train Loss in Epoch 28: 0.0083\n","Average Test Loss in Epoch 28: 0.7345\n","Average Test Acc in Epoch 28: 0.5756\n","Pearson Coeff in Epoch 28: -0.01275095297448143\n","Average Train Loss in Epoch 29: 0.0070\n","Average Test Loss in Epoch 29: 0.7228\n","Average Test Acc in Epoch 29: 0.5699\n","Pearson Coeff in Epoch 29: -0.010738972002671059\n","Average Train Loss in Epoch 30: 0.0058\n","Average Test Loss in Epoch 30: 0.7384\n","Average Test Acc in Epoch 30: 0.5746\n","Pearson Coeff in Epoch 30: -0.014365453459400542\n","Average Train Loss in Epoch 31: 0.0048\n","Average Test Loss in Epoch 31: 0.7270\n","Average Test Acc in Epoch 31: 0.5708\n","Pearson Coeff in Epoch 31: -0.01160351295848988\n","Average Train Loss in Epoch 32: 0.0044\n","Average Test Loss in Epoch 32: 0.7284\n","Average Test Acc in Epoch 32: 0.5734\n","Pearson Coeff in Epoch 32: -0.013472051296738327\n","Average Train Loss in Epoch 33: 0.0056\n","Average Test Loss in Epoch 33: 0.7188\n","Average Test Acc in Epoch 33: 0.5686\n","Pearson Coeff in Epoch 33: -0.00952966769478103\n","Average Train Loss in Epoch 34: 0.0051\n","Average Test Loss in Epoch 34: 0.7328\n","Average Test Acc in Epoch 34: 0.5728\n","Pearson Coeff in Epoch 34: -0.014258350328160824\n","Average Train Loss in Epoch 35: 0.0052\n","Average Test Loss in Epoch 35: 0.7503\n","Average Test Acc in Epoch 35: 0.5829\n","Pearson Coeff in Epoch 35: -0.013481927715847445\n","Average Train Loss in Epoch 36: 0.0056\n","Average Test Loss in Epoch 36: 0.7332\n","Average Test Acc in Epoch 36: 0.5736\n","Pearson Coeff in Epoch 36: -0.011264977647855326\n","Average Train Loss in Epoch 37: 0.0046\n","Average Test Loss in Epoch 37: 0.7137\n","Average Test Acc in Epoch 37: 0.5610\n","Pearson Coeff in Epoch 37: -0.0145935856302308\n","Average Train Loss in Epoch 38: 0.0048\n","Average Test Loss in Epoch 38: 0.7094\n","Average Test Acc in Epoch 38: 0.5559\n","Pearson Coeff in Epoch 38: -0.015018139630774197\n","Average Train Loss in Epoch 39: 0.0050\n","Average Test Loss in Epoch 39: 0.7175\n","Average Test Acc in Epoch 39: 0.5611\n","Pearson Coeff in Epoch 39: -0.01683060454278484\n","Average Train Loss in Epoch 40: 0.0056\n","Average Test Loss in Epoch 40: 0.7006\n","Average Test Acc in Epoch 40: 0.5604\n","Pearson Coeff in Epoch 40: -0.014162116495078265\n","Average Train Loss in Epoch 41: 0.0071\n","Average Test Loss in Epoch 41: 0.7132\n","Average Test Acc in Epoch 41: 0.5651\n","Pearson Coeff in Epoch 41: -0.011043106471982304\n","Average Train Loss in Epoch 42: 0.0057\n","Average Test Loss in Epoch 42: 0.7186\n","Average Test Acc in Epoch 42: 0.5623\n","Pearson Coeff in Epoch 42: -0.01730310071553439\n","Average Train Loss in Epoch 43: 0.0046\n","Average Test Loss in Epoch 43: 0.7365\n","Average Test Acc in Epoch 43: 0.5735\n","Pearson Coeff in Epoch 43: -0.015166682443291847\n","Average Train Loss in Epoch 44: 0.0053\n","Average Test Loss in Epoch 44: 0.7442\n","Average Test Acc in Epoch 44: 0.5783\n","Pearson Coeff in Epoch 44: -0.016477045219500264\n","Average Train Loss in Epoch 45: 0.0049\n","Average Test Loss in Epoch 45: 0.7381\n","Average Test Acc in Epoch 45: 0.5715\n","Pearson Coeff in Epoch 45: -0.014855397963026226\n","Average Train Loss in Epoch 46: 0.0045\n","Average Test Loss in Epoch 46: 0.7301\n","Average Test Acc in Epoch 46: 0.5700\n","Pearson Coeff in Epoch 46: -0.014198007048672636\n","Average Train Loss in Epoch 47: 0.0046\n","Average Test Loss in Epoch 47: 0.7421\n","Average Test Acc in Epoch 47: 0.5746\n","Pearson Coeff in Epoch 47: -0.014543215716162435\n","Average Train Loss in Epoch 48: 0.0051\n","Average Test Loss in Epoch 48: 0.7382\n","Average Test Acc in Epoch 48: 0.5737\n","Pearson Coeff in Epoch 48: -0.018195214360679147\n","Average Train Loss in Epoch 49: 0.0058\n","Average Test Loss in Epoch 49: 0.7369\n","Average Test Acc in Epoch 49: 0.5736\n","Pearson Coeff in Epoch 49: -0.0187835210579788\n","Average Train Loss in Epoch 50: 0.0060\n","Average Test Loss in Epoch 50: 0.7327\n","Average Test Acc in Epoch 50: 0.5713\n","Pearson Coeff in Epoch 50: -0.016376628604220102\n","Average Train Loss in Epoch 51: 0.0047\n","Average Test Loss in Epoch 51: 0.7333\n","Average Test Acc in Epoch 51: 0.5698\n","Pearson Coeff in Epoch 51: -0.021823389604178886\n","Average Train Loss in Epoch 52: 0.0039\n","Average Test Loss in Epoch 52: 0.7330\n","Average Test Acc in Epoch 52: 0.5661\n","Pearson Coeff in Epoch 52: -0.021463282019656534\n","Average Train Loss in Epoch 53: 0.0048\n","Average Test Loss in Epoch 53: 0.7222\n","Average Test Acc in Epoch 53: 0.5625\n","Pearson Coeff in Epoch 53: -0.018696313913313105\n","Average Train Loss in Epoch 54: 0.0056\n","Average Test Loss in Epoch 54: 0.7278\n","Average Test Acc in Epoch 54: 0.5663\n","Pearson Coeff in Epoch 54: -0.017137352732088478\n","Average Train Loss in Epoch 55: 0.0051\n","Average Test Loss in Epoch 55: 0.7280\n","Average Test Acc in Epoch 55: 0.5682\n","Pearson Coeff in Epoch 55: -0.018891591467467665\n","Average Train Loss in Epoch 56: 0.0040\n","Average Test Loss in Epoch 56: 0.7340\n","Average Test Acc in Epoch 56: 0.5694\n","Pearson Coeff in Epoch 56: -0.019951237621963408\n","Average Train Loss in Epoch 57: 0.0041\n","Average Test Loss in Epoch 57: 0.7418\n","Average Test Acc in Epoch 57: 0.5753\n","Pearson Coeff in Epoch 57: -0.019676796008937927\n","Average Train Loss in Epoch 58: 0.0041\n","Average Test Loss in Epoch 58: 0.7390\n","Average Test Acc in Epoch 58: 0.5707\n","Pearson Coeff in Epoch 58: -0.020598112045259128\n","Average Train Loss in Epoch 59: 0.0043\n","Average Test Loss in Epoch 59: 0.7237\n","Average Test Acc in Epoch 59: 0.5626\n","Pearson Coeff in Epoch 59: -0.018634062292781854\n","Average Train Loss in Epoch 60: 0.0043\n","Average Test Loss in Epoch 60: 0.7294\n","Average Test Acc in Epoch 60: 0.5690\n","Pearson Coeff in Epoch 60: -0.019200184562833688\n","Average Train Loss in Epoch 61: 0.0048\n","Average Test Loss in Epoch 61: 0.7250\n","Average Test Acc in Epoch 61: 0.5645\n","Pearson Coeff in Epoch 61: -0.017169600118991837\n","Average Train Loss in Epoch 62: 0.0046\n","Average Test Loss in Epoch 62: 0.7429\n","Average Test Acc in Epoch 62: 0.5717\n","Pearson Coeff in Epoch 62: -0.02105909092450887\n","Average Train Loss in Epoch 63: 0.0040\n","Average Test Loss in Epoch 63: 0.7397\n","Average Test Acc in Epoch 63: 0.5708\n","Pearson Coeff in Epoch 63: -0.020242108494791737\n","Average Train Loss in Epoch 64: 0.0042\n","Average Test Loss in Epoch 64: 0.7207\n","Average Test Acc in Epoch 64: 0.5620\n","Pearson Coeff in Epoch 64: -0.02208794678246374\n","Average Train Loss in Epoch 65: 0.0042\n","Average Test Loss in Epoch 65: 0.7232\n","Average Test Acc in Epoch 65: 0.5612\n","Pearson Coeff in Epoch 65: -0.019729349599685995\n","Average Train Loss in Epoch 66: 0.0048\n","Average Test Loss in Epoch 66: 0.7218\n","Average Test Acc in Epoch 66: 0.5619\n","Pearson Coeff in Epoch 66: -0.01763264710866364\n","Average Train Loss in Epoch 67: 0.0045\n","Average Test Loss in Epoch 67: 0.7217\n","Average Test Acc in Epoch 67: 0.5601\n","Pearson Coeff in Epoch 67: -0.019551360501773796\n","Average Train Loss in Epoch 68: 0.0050\n","Average Test Loss in Epoch 68: 0.7119\n","Average Test Acc in Epoch 68: 0.5567\n","Pearson Coeff in Epoch 68: -0.018149361915137767\n","Average Train Loss in Epoch 69: 0.0042\n","Average Test Loss in Epoch 69: 0.7361\n","Average Test Acc in Epoch 69: 0.5679\n","Pearson Coeff in Epoch 69: -0.019791509404197764\n","Average Train Loss in Epoch 70: 0.0047\n","Average Test Loss in Epoch 70: 0.7192\n","Average Test Acc in Epoch 70: 0.5590\n","Pearson Coeff in Epoch 70: -0.02263321301328681\n","Average Train Loss in Epoch 71: 0.0050\n","Average Test Loss in Epoch 71: 0.7246\n","Average Test Acc in Epoch 71: 0.5600\n","Pearson Coeff in Epoch 71: -0.01887427602274251\n","Average Train Loss in Epoch 72: 0.0043\n","Average Test Loss in Epoch 72: 0.7290\n","Average Test Acc in Epoch 72: 0.5655\n","Pearson Coeff in Epoch 72: -0.01975682382762401\n","Average Train Loss in Epoch 73: 0.0045\n","Average Test Loss in Epoch 73: 0.7275\n","Average Test Acc in Epoch 73: 0.5611\n","Pearson Coeff in Epoch 73: -0.022303661384655567\n","Average Train Loss in Epoch 74: 0.0044\n","Average Test Loss in Epoch 74: 0.7345\n","Average Test Acc in Epoch 74: 0.5697\n","Pearson Coeff in Epoch 74: -0.013561320865527042\n","Average Train Loss in Epoch 75: 0.0046\n","Average Test Loss in Epoch 75: 0.7555\n","Average Test Acc in Epoch 75: 0.5764\n","Pearson Coeff in Epoch 75: -0.02202401637066137\n","Average Train Loss in Epoch 76: 0.0033\n","Average Test Loss in Epoch 76: 0.7241\n","Average Test Acc in Epoch 76: 0.5617\n","Pearson Coeff in Epoch 76: -0.019334440020621756\n","Average Train Loss in Epoch 77: 0.0037\n","Average Test Loss in Epoch 77: 0.7241\n","Average Test Acc in Epoch 77: 0.5612\n","Pearson Coeff in Epoch 77: -0.01860456967621103\n","Average Train Loss in Epoch 78: 0.0041\n","Average Test Loss in Epoch 78: 0.7257\n","Average Test Acc in Epoch 78: 0.5617\n","Pearson Coeff in Epoch 78: -0.020251074135047984\n","Average Train Loss in Epoch 79: 0.0039\n","Average Test Loss in Epoch 79: 0.7314\n","Average Test Acc in Epoch 79: 0.5637\n","Pearson Coeff in Epoch 79: -0.01813485602271905\n","Average Train Loss in Epoch 80: 0.0036\n","Average Test Loss in Epoch 80: 0.7216\n","Average Test Acc in Epoch 80: 0.5600\n","Pearson Coeff in Epoch 80: -0.02119288567892971\n","Average Train Loss in Epoch 81: 0.0040\n","Average Test Loss in Epoch 81: 0.7204\n","Average Test Acc in Epoch 81: 0.5569\n","Pearson Coeff in Epoch 81: -0.01891174788038295\n","Average Train Loss in Epoch 82: 0.0040\n","Average Test Loss in Epoch 82: 0.7121\n","Average Test Acc in Epoch 82: 0.5535\n","Pearson Coeff in Epoch 82: -0.025232018269285464\n","Average Train Loss in Epoch 83: 0.0041\n","Average Test Loss in Epoch 83: 0.7225\n","Average Test Acc in Epoch 83: 0.5599\n","Pearson Coeff in Epoch 83: -0.017284696851279386\n","Average Train Loss in Epoch 84: 0.0038\n","Average Test Loss in Epoch 84: 0.7079\n","Average Test Acc in Epoch 84: 0.5509\n","Pearson Coeff in Epoch 84: -0.01465603118938496\n","Average Train Loss in Epoch 85: 0.0041\n","Average Test Loss in Epoch 85: 0.7082\n","Average Test Acc in Epoch 85: 0.5533\n","Pearson Coeff in Epoch 85: -0.020592868840659445\n","Average Train Loss in Epoch 86: 0.0037\n","Average Test Loss in Epoch 86: 0.7177\n","Average Test Acc in Epoch 86: 0.5548\n","Pearson Coeff in Epoch 86: -0.013848488095631058\n","Average Train Loss in Epoch 87: 0.0046\n","Average Test Loss in Epoch 87: 0.7191\n","Average Test Acc in Epoch 87: 0.5614\n","Pearson Coeff in Epoch 87: -0.020433791133147864\n","Average Train Loss in Epoch 88: 0.0039\n","Average Test Loss in Epoch 88: 0.7168\n","Average Test Acc in Epoch 88: 0.5562\n","Pearson Coeff in Epoch 88: -0.0209650485342702\n","Average Train Loss in Epoch 89: 0.0039\n","Average Test Loss in Epoch 89: 0.7073\n","Average Test Acc in Epoch 89: 0.5538\n","Pearson Coeff in Epoch 89: -0.01930070775427866\n","Average Train Loss in Epoch 90: 0.0033\n","Average Test Loss in Epoch 90: 0.7176\n","Average Test Acc in Epoch 90: 0.5562\n","Pearson Coeff in Epoch 90: -0.018268362264179574\n","Average Train Loss in Epoch 91: 0.0036\n","Average Test Loss in Epoch 91: 0.7092\n","Average Test Acc in Epoch 91: 0.5495\n","Pearson Coeff in Epoch 91: -0.01695721266439781\n","Average Train Loss in Epoch 92: 0.0035\n","Average Test Loss in Epoch 92: 0.7220\n","Average Test Acc in Epoch 92: 0.5597\n","Pearson Coeff in Epoch 92: -0.020730753494440576\n","Average Train Loss in Epoch 93: 0.0032\n","Average Test Loss in Epoch 93: 0.7041\n","Average Test Acc in Epoch 93: 0.5497\n","Pearson Coeff in Epoch 93: -0.023294490975846783\n","Average Train Loss in Epoch 94: 0.0034\n","Average Test Loss in Epoch 94: 0.7133\n","Average Test Acc in Epoch 94: 0.5538\n","Pearson Coeff in Epoch 94: -0.021937212324890532\n","Average Train Loss in Epoch 95: 0.0036\n","Average Test Loss in Epoch 95: 0.7207\n","Average Test Acc in Epoch 95: 0.5584\n","Pearson Coeff in Epoch 95: -0.023256931584865477\n","Average Train Loss in Epoch 96: 0.0038\n","Average Test Loss in Epoch 96: 0.7115\n","Average Test Acc in Epoch 96: 0.5523\n","Pearson Coeff in Epoch 96: -0.023386304406968088\n","Average Train Loss in Epoch 97: 0.0044\n","Average Test Loss in Epoch 97: 0.7186\n","Average Test Acc in Epoch 97: 0.5548\n","Pearson Coeff in Epoch 97: -0.017680577356088172\n","Average Train Loss in Epoch 98: 0.0044\n","Average Test Loss in Epoch 98: 0.7276\n","Average Test Acc in Epoch 98: 0.5588\n","Pearson Coeff in Epoch 98: -0.027775749690502043\n","Average Train Loss in Epoch 99: 0.0043\n","Average Test Loss in Epoch 99: 0.7179\n","Average Test Acc in Epoch 99: 0.5514\n","Pearson Coeff in Epoch 99: -0.024401396445606256\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"OV-ZxbuwBw76","colab_type":"code","outputId":"6b2f1823-f5b0-4f1b-dc49-57921d85b898","executionInfo":{"status":"ok","timestamp":1582140724286,"user_tz":0,"elapsed":1897,"user":{"displayName":"Andy Wang","photoUrl":"","userId":"02776860930356410397"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# plot stats\n","plt.plot(range(len(train_losses)), train_losses)\n","plt.xlabel(\"Epochs\")\n","plt.title('Train losses')\n","plt.show()\n","\n","plt.plot(range(len(test_losses)), test_losses)\n","plt.xlabel(\"Epochs\")\n","plt.title('Test losses')\n","plt.show()\n","\n","plt.plot(range(len(test_acc)), test_acc)\n","plt.xlabel(\"Epochs\")\n","plt.title('Test acc')\n","plt.show()\n","\n","plt.plot(range(len(test_pearson)), test_pearson)\n","plt.xlabel(\"Epochs\")\n","plt.title('Test pearson')\n","plt.show()\n"],"execution_count":18,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXQAAAEWCAYAAAB2X2wCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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zb9Ra7d2GMYfepHlZXF7CqquCMLPpjtuvjT3EM7u9t6mV+cXaot1VdmMUDH7qQ/3fV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\ncHsUax6sAAJXWkqE0D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w512pfouGdzro6s0vzuHBRJR19fnYm6Mx1hlU6Q2QTmeqM/nj3IAc7+jl3fnnS/pR9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\nMjPQa0avlMogGRnoe31BCnN0G0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size 432x288 with 1 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j113wr8A1jTMIY8xpwxP68dcuhvgl2tUWprw7ymV+8EBHLdVEOTnUxlOkaChe2SXAq\nis9bJSGAmYCxIwirTXNNiOFpyyLw+8R1C3mFwIkPOC68/KKyruGF9f0ZmUq6v8tyXEMbPQ38WqKW\n5bJSmUMDkwmyBtsdtT6ylR47OsgffPeF1V7GqrMUQrAJ6PI877aPFT3HGJMGxoDmMt8LgIh8UET2\ni8j+gYGBJVj26nCkf9Ktir1yRzNfef/lfLRImmUxGj158JFZxlQ6FOuX82LPGAGfsGdDtMS7lp9m\n2xJoKcMKWgmcDqRDUwkaq4NuoN477rPPHqvpBPW9mUMv945z7acf4tEjg/O+9vBUko0NYcJBXxnB\n4lhOlbaIWCmkK2QReEdjHh9cH0Jw/0v9fP2nJ4klK9vCWTPBYmPMncaYfcaYfa2trXO/4QxkIp6i\ndyzu9tEHuHZ3a0E1bCm8BVHlZA3VR4LEUhmS6Zk5wgdPjbO7vbbs8ZDLwZnoGkpnDccHp11rBRzX\nmuMasjbB3W211IYDOZlDTx6zUjhfG5y/r96pZG6uqZo1RpBMZ+mfSBT8raxkLUGfVwhWubXFH37v\nBR56pX/Rn+PtMVXJLIUQ9ABbPM8328eKniMiAaAeGCrzvesGp5Fbfp+ccnEGyED5WUOAm0JqjOHg\nqbGShWQrRdMZKARgxW+8QuC1CPonElQFfNRFAmxprM6JETxzchQgZ6hQuThCkN8OO5/TY3GMKWyh\nsa3ZEoJMdvnbUTsWQdAvq1rRPJlI87UnT/K9Zxe/VTjiq0KweJ4CdovIdhEJYQV/78k75x7gNvvx\nO4AHjeVQvQd4l51VtB3YDfx0Cda05BhjSKQXZz4edoVgYW6ZhojXNVSeRQDkbGaDk0nOW2UhaK+r\nQoSyAuQrgbP5D04mcmobvFlXfeNx2uushnZbmiLunAaAZ7usFiGnx+ZuE5HP8PSMEMxmEfQU6eQK\ncOm2RhLprJt9tpz0jccJBXycvaFuVS0CxxV2yDNJb6G4FsFUZbfzXrQQ2D7/DwH3Ai8D3zLGHBSR\nT4jIW+3T7gKaReQI8FHgDvu9B4FvAS8B/w78pjHmjHTW/b/nerjqLx5clBgc6Z8kFPCVNe2rGI2e\nu9VwGa6d/DYJTkXxuXNUFC83v3TZVv7p/Ve461ttvJt/gWsonsYYYwuBdd6Wxmq6R6zg8MBEwo0X\nzLfQyhjDiOsaWpgQ3HzuBm4+t51P3/sKB0+Nzev68+X0eJwNdWE6W2qW1SIYnEzMGnh30mWPDU6R\nzmRLnlcOjhAMq0WweIwxPzTG7DHG7DTGfNI+9jFjzD3247gx5p3GmF3GmMuNMcc87/2k/b6zjDE/\nWor1LAfdw7GcFMOFcLhvgp2tUfxltnTOZ74WQX4H0gNdo/h9suquofpIkGt2t6zqGrw01sz8XPOF\nIJM1TCbS9E8kaKu1Mna2NBw1T2gAACAASURBVFUTT2UZmEzw7EnLGmirreL0WKEQDEwkSrptxuNp\n0llDY7XjGsq1KHrHYtxz4BQ/fKGX/7QD0fmttEWET73tAhqrQ/z2N55b1qBn75gtBM2WEHpjT6UY\nnU66ay+HV06Pc/kn7+exWVpnOCKUTGcXHR9xLIGRMru/rlfWTLB4tUk6E6xm+YNJZ7L885Mn3GlX\n+Rzun1ywWwigfgHBYrA2HIBnu0bZ015LdUhbTHnxWgTN0VwhAEtI+8cTtDkWgT0joms4xjMnRwn6\nhTec3VYQI4glM1z/Vw/xf/6zuNvG+VtqjoZoioaIp7JMe+Yf/Mk9L/Hhrz/Lf/vnZ/jesz1sbaou\n+ntvrAnxmV+8kCP9k/zFj15eyI+gLPrG42yoD7OtuYasmbFSZuPOR47x3rueLHujvffFPrJmpjtt\nMbzDeA73L7y4LZHOMJmwft7LMXBoLaFCUCblCMFTx0f4o++9WPQOaCqRpnsktighqAr4qbYtgfkE\ni8diKYwxHOga5aItq+sWOhOJhPzuz7OxulAIesfiTCbSbpNAZ3xo98g0z5wcYe/GerY0VTMeT+fc\nkZ8cnmYqmeEHL/QWva7zt9RYHXIzqbwW5+H+Ca7d3cK9//06fvDha/juf3tdye9w7e5W3n/1dr7y\n+IllmVFgjLEsgnrLIoDyMoee6xrFmJn6lbl48JU+wLKGSnFyaNqdR3F4EXEC75jPUXUNKeWQSlvm\n/WzZBc4f02AR99HRATtQ3L64/P2GSBC/Twj653YvOZ0sx2Mpjg9NMx5Pc9GW0j13KpliRW6OkDot\nFdrs4LYzPvS1wSme7x7l4i0Nrkh4rQJnQ36ua9QdzenFtQhqqmjKm4uQyRq6hmPs3VjHWRtqOXdj\n/ZxZVm+7xCrBeb576WMFo9MpkuksG+osiwDgxBzpstms4QV7LeXEL/on4hywzz9VxM3mcGJ4ir0d\ndWxqiCzKIvDe1A1X+OxnFYIySZVhETi++GKtApzNZNcCU0cdGqpDRIL+WfvWO4SDfqoCPsZjKQ50\nWSmOF6oQFMURgqYiriEnO8XZ7CMhPy3RKu5/uY94Kssl2xrdATg5QmDXGhgDP361sAjScZc01gQL\nJqWdGo2RzGTZ3lx+h9jd7VECPuHFZQgaO6mjG+rDtERD1IT8HJ8jYPza0BQTtuulHIvgx69YP6PW\n2ip6S7idUpksp0bjbGuuZldblEOL6Hvk/PxFNEagQlAmTmCsHCEolg9+uH+SoF/Y1rywjCGHhupg\nWfEBBycX/rmuUapD/gXXMKx3XCGoKRSCw/2OEMzckW9pivBij7W5WRaB9drpHIsgRnXIT3tdlevy\n8DLksQhc15B97Jh9t719Hq3CqwJ+9rTXlu2GmQ+OwG2ot1JotzXX5MxL/tELve6cbYfnu62bj23N\n1WWJ0wOv9LGxPsx1u1tzqpi99IzEyGQNW5uq2dMe5ejA5IJrKJxMoc2NEY0RrPYC1grlWARO4Vax\nVgFH+ifY0RIluMjWz001IWqqyheCeo8QnLepfsEZS+sdZyMuFiNw7jrbPDOPnThBa20Vmxsj7mtO\nczqwLIItjdW84ew2Hjk0WJBlMzKdJBz0EQn5XUvEuTM9vgAhADh3Yx0He8YW1PdoNlyLwP6enS3V\nbvZOOpPlE//2En97/6GcO+sDXWNEgn7eeuFGXhucYiqRLvxgm0Q6w08OD/KGc9rY2BCmbzxeNDXU\nCRRva65hd1vtojKHnLXuaIkuOEZwbGCS7AoU8y03KgRl4gSLZ4sRzLiGilsEuxYZHwD47zfu5lNv\nu6Ds8+sjQYYmk7x0alzjA7NwTkcdZ7XX5gh1tCqA3ycMTCQIB33UesZqOplDF29pQESoCweIBP0F\nMYItTRHecHY7k4k0Tx0fzrnm0GTSzViqrQoQ9ItrEbw2OEVNyD/vorvzNtUzNJWkb3z+xW2zcXo8\njs9TBLituYaukWnSmSz3v9xH71icrIEfH5pp+/BCzxjnbarjgs0NGGOlhpbiyWPDTCcz3HB2Ox31\nEbLGKoDM56RthWxrrnbjbQsNGA/bqaM7WmsWVEfQOxbjxr95uGQywFpChaBMHItgtjqCMbvlc75r\nKJ7KcHJ4elEZQw672mq5amdz2efXhQM81z1KMpNVIZiFX7tuB/d+5LqcY84GD7hVxQ6ORXCJ3YRO\nRGivq3JdQ8YYukdibG6s5updzYQCPh54Obc3zsh00q1hEBG7utja/I4PTdHZUlNWLMiLUyPiFA8u\nFafHYrREq1yh7GyuJpWxMom+8vgJNjVE7LiJ9R3TmSwHT41xweYGd02zuawefKWfcNDHVTub6bA7\nrBbLHDo+NE046KOttord7Zabc6EB45HpJLXhAG21YeKp7LxrME4OTZOdR0bUmYwKQZk4Zv1CLIJT\nozGMmekfv5LUR4Lu2jVQPH8c91B7bW4h196NdYjA1TtnCuPa68Kua2h0OsVkIs2WpmqqQwFet7OZ\nB17py3HZDE0l3WwhsCajDXssgs4FjBI9p8Na11JvTqfHE3R4itmczKH7XurjsaNDvOfKrdxwdhuP\nvDpAMp3lUN8k8VSWCzbX01EfpqkmVFKcjDE88Eof1+xqIRz0s7Hesrac0ZxeTgxNs63JEshoVYCN\n9eEFWwQjdnsPp6vvfK0Cx2I5NrD2B/WoEJRJKmP9Dzw8S0+S8RJC4Jjp7XXhgvcsN85G1hKtYmP9\nyl9/reP8/Nrqcl00F2xu4Jn/8UbO3zxTl9FeF3YtAidjaEujtandcHYbJ4amOTowE2AdmUrS5CkS\ndEZmpjJZukdi88oYcqipCrC9paZkcDaTNdxz4NS8s2ROj8Vy/n477bV97sHDhAI+fmnfFm44p40J\n2wXmBIov2Gy5zs7dWFdSnO45cIqu4RhvOLsdYFaL4OTwFFs9CRe722sXbBEMTyVprA65rVvm+zNx\nhOCoCsH64JFDA3z/udk7GXpjBKWCQ44QTCbSOT2JnB407XUr32TNyYW/aEv9vN0MyszPr622UES9\nvZ/A+v32jccxxrj9h5y+Um84x9rkvNlDwwUWgdVvqMvuJjrfQLHDeRvreanEpvt/HzvOh7/+LG//\n4mPzGi5z2i4mc2irrSIc9DE6neItF3TQHK3imt0thAI+7n+5jwPdY9SFA27x2d6NdRzqm8gJmI9N\np/jtbzzLb3/jOS7YXM9bLuwArPqXaFWgwCIwxnByeJptHst6d1uUI/0Lyxwa8TT8c57PB+f/65PD\n0yW7CawVVAiAux87zmfvOzTrOc4fcCZrmIgXz34Yi6XcQi+vVeC4C9pW0SK4cJbh7UppXNdQGSLe\nXhcmkc4yHku7mSyOEGxqiLC7LcpPDltV5057g6a8PkfDk0m3YnchriGw4gQ9o7GCO9yTQ9P89b2v\nctGWBgYmErzjC4+X1cFzOplmPJ7OEQKfT9hmz0y+7apOAKpDAa7e2cwDL/fzfPeoaw2AJU6pjHFT\ncQ/1TXDT3z7MD57v5aNv3MN3fuN1bgEkQEd9uMAi6J9IEE9lc1Kw97TXkkhnFzQxbWQqZVkE1bk1\nHOUyYP9/ncqYZanmXklUCIBEOjvnmECv4hfzJRpjGIul3DiAN6jcNx4vyDpZKVyLYKsKwUKYEYK5\nRdw55/R4nK6RaRqrrTtbh2t2t/DT14aJpzJuszOvRdBcE2IikebV05arYcEWgd1d1uuKMcZwx3ef\nx+8TvvDeS/jWf72KrDG84wuPzVn1ezovddTh0s5GXrezOSf2dMM57ZwcnubgqXEu8LjN3IBxzzjx\nVIYPfe0ZMln4f795NR++YXdBWnVHQ6SglsBJV93qcZk5mXgLKSyzLLKZYr6FuIZCAWvdxwZWd1DP\nYlEhwLo7m4inZx3Incpk3X40xSqHY6kM6axhe0vUPsdjEUwkCrJOVorrdrfy3iu3clmZc5GVXNwY\nQRlpnM4dc9943E4dzU0OuHZ3C4l0lv3HR9xOozkWgV1L8MzJEerCgZzRpPNhJktnZoP/5lNdPHZ0\niD9489l01Ec4p6OO7/zG64iE/Pzuvzxf4NrwBrVPe6qKvfz5L5zPVz9wRc6xG85pcx97haCzuYaa\nkJ+Dp8b48x++zKG+Sf7mFy90RSufjfXhAteQU8CW7xqCmaK/coklM8RSGRprQtRHgojMv81E/0Sc\ni20RnC1O8NTxYU6V0aBvNVEhwLIIYHbTMJUx7v8IxQLGTsbQ9pbqgs/qG4+XtZEsBxvqw/zZz58/\nr2pkZYaZYHEZFkHtjEXQPRJzU0wdrtjeTNAv/OTIQEmLAOCZEyNsb40u+MahoTrEpoYIL9oWwcFT\nY3zyBy9z5Y4m3n3ZVve8LU3V/Mlbz+Pl3vGcwTanRmPc9NlH3E6mTgA83yIA3PnODh31EVeILvC4\nI30+Ye/GOv71+V6+8vgJbr9mO9ftKT1ytqM+wuBkIifWdnJ4Gr9P2NQ4M5OhNhy0vusc6bJPHBvi\nngOn3OdOPKCxOoTfJ9RHgvMuKuufSLC7PUpLNFTSIhidTvKef3ySv7v/cNHXzxRUCMC1BGarEUim\ns66fuJgJOSME1h3KUJ5FsBrxAWXxXLGjmWt3t7gFZLPhZBadHovTMxJjc957aqoCXLy1kUcPDxa3\nCGxRGJpKsn2RrUisLJ0xvvdsN2//wmPUVAX49NsvLNi4bz63nRvPaeez9x+ia3iagYkE7/3Skxzu\nn+QfHj7GQ6/05/QZKof3XLGNK3c05aSbWmuqZ3gqyd6OOn7vlrNm/Qwnc8g74+HE0DQbG8IFbqQr\ndzTz2NGhWQPGf//QEf7knoPuc2/nV4Cm6tkHA+WTSGcYnU7RVhtmR0u0pEVwz4FTJDPZBc2zXklU\nCJixCAaLuHwckpms6wMuFk8Ys83KLU0R/D7JcR/1j8cL8tCVtcFFWxr4pw9cQVUZE+HCQT8N1UGe\n7x4jmckWWAQA1+xq4eCpcTeNND9ryGGhgWKHczfWc2xgio988wAXbm7gX3/rmpy0SwcR4RO3notf\nhDu++zzvu+tJesfifO32Kzh7Qy2/9+0DvHRqnLpwoOw5Fr98xVa+8cGrCiyaa3e30FwT4nPvvmjO\nn2exWoITw9NugNrLdXtaGJ1OzWoVHB+aYmgqydDkTJ0HzPzMG2tC88oaGrBTR9tqq9jZVuP2hsrn\nX/Z3A1YDvjMZFQIgkZq7ajiVyVIfCRIO+or+wTjDXxoiVhaCc3cxmUgzlcwU5KEr65P22jBPn7Ba\nSRQbSepMZvu3508hQs64Tm8L7IUGih2u3GHFhN5/9Xa+evsVs7aq2NgQ4XduOov/PDLEsYEp/vFX\n9vG6XS383bsuZjye5gcv9NJRP7dFNBc3nNPO/v9xY1kdeIvVEpwYmiratPGaXdbP9CeHCzu8gmXN\n99gzpp2gspPw4VhkjdXBWWuE8nFqCNrqqtjREmV4KlngKXjl9Dgv9IyxqSHCwERi1l5Lq40KAbh+\nyGI94x2S6Swhv6+kCem4huojQaswyBaV/vHVqyFQVp62uipGHOuwsXDzvGBTPbXhAMcGplz/tEO9\nPWsCFi8EV+xo5sDHb+JjP7e3rEaHt72ukw9cs51/vG2fK1ZnbajljlvOBqB9iYoRy417OBaB45Y6\nPjjF6HTKHUjjpTlaxXmb6njkcPGRmN0jVisImAkqj+S5hhqrQ/PKGnJTwmvD7GyzflfHBnPdQ/+y\nv5ugX/j163cCLOuc58WiQsCMa2hoFiFIZbIEAz4aSwwZd4SgLhJwC4PAU1WsrqGKwAmoipAT1HQI\n+H28zu4VlZ8V5POJe2yxriHItTbmwu8T/vgte3l9XgD3v7yuk1++YitvOb9j0euZD5GQ5WZzsm2+\n80w3PoE3nVd8HdfubuWZEyPu6Ekv3klqTt3E8FQyxyJrsl1D5XZtHbCLydpqLYsA4Gj/zHVSmSz/\n79kebji7nUvs1O0TZ7B7SIUArxAUvyMwxpDKGIJ+X84m78URgtpwkKbozDlO9aG6hioDJ47UXhsu\n6Qe/Zre12TblVSY7x5prQjnFVauJzyf8+S+czy9etmXFr91Rb9USZLOG7z7Tw9W7WkoGrK/d3UI6\na3iiyND744PWnXhnc7XrGhqZTlIfCRKwraXGmhCJdJbYLCnkXvonEvjEskY2N0YI+X0c9VgED73S\nz9BUknfu2+z2ZVpsnGCpW4t7qXghSGeybrbBYAnT0OkzFPKLe+eQz3gsRW3Yalvs9IyB1a0qVlYe\nx4UyW5bRtbZPu5gQbG2qZq+dflnpWLUEMZ54bYie0RjvuHRzyXMv3dZIJOgvGic4PjRFbVWAK3c0\nc8TuSzQ8laTJM3uiaZ7Vxf3jCZqjVfh9QsDvY1tzdY5F8C9Pd9NaW8Xr97QSrQrQEq3ixODiXENf\nffIkr/+rh5ZlmlrFC0HC0/tksEj/c5jpMxQK+KxAcBHLYTyWyjEzx2IpUpksfeNxIkH/qlQVKytP\nux2ULZYx5LCtuZrzNtVx9obCDf+v33khn3vXxcu2vrVER0OY3rE433m6h2hVgJv2bih5blXAz5U7\nmtwWHl6OD03T2VLD7vZahqeSDE4m7BbgM0LQYLvkRsoMGPdP5NYG7WyNujGCx44O8sDLfbz9ks2u\nxdHZXJ3jogKr0OzpEyNlXQ/gQNcok/G0u9alRIXAIwRDJdJHU/Y5Qb/PbQOQP21qLJZyzflmTxOr\nvokEbXVV2vCtQnBcQ5tnaTkuItzzm9fwkTfuKXitwdMNs9LpqI8wFkvxwxd6+dnzO4iEZk85vXZ3\nK8cGpwr6/jjZRnvcdhQTDE+lcmI0+Y3nkuksjxdxMzn0TyRyhGBHaw0nh6bpGp7mw19/lu0tNXzo\nDbvc163Rnrnr+v1vP89Hv/Vc2S6fA12jXLilYVn2EhUCO2Ooyc70KfZLccrvg36f+z9pfhXiWI5F\nYP2BDE8ltYagwuhsqaE2HHADhKXIL+xSCtlop5DGUhnePotbyOG6PZbL7dEjM1aB09K7s7mGPc4g\nm75JRqeTOWNJG/OE4KtPnODd//gEP341d5iQw8BEIqcj7c7WKOms4X13PUksmeEf3ndpTp+p7S3V\nnB6Pu8Nv+sfjvDY4xYmhaV7qnXt2xEQ8xZGByWVrHrkoIRCRJhG5T0QO2/9tLHHebfY5h0XkNs/x\nT4pIl4isWkPvuF1DsLEhTDprGI8VZh14XUPOnUN+Udl4PNc1BNbs4n7bIlAqg/pIkBf+581cf1bb\n3Ccrs+LULmxtquayzqJbSw47W6N01Id55NBMnKDbHnbf2VJDW20VdeGAbREkc2I0+TECZ/zk395/\nuODmMJM1DE7m/n+9o9UKCB8fmubT77iwoFbCCRifGLbcQz/1jC390Qun5/xuL/SMYQxcuKV4b6bF\nsliL4A7gAWPMbuAB+3kOItIEfBy4Argc+LhHMP7VPrZqOBbBpgbrj65YdbHjBgr5fSU7FXotgubo\njFhYfYbUIlCU+eJ08n3bJZvKcoeICNftbuXRI4Pu4PvjdsVvZ3M1IsLu9loOdI+SSGdzXHB1duO5\nkakkp8fiPH1ihD3tUZ7rGuXhQ7kB6KGpBFmT24hwV1uU6pCfX7t2Oz97QWGKqzPIx8lgeuq1YSJB\nP1dsb+KHL/TO6R460GVVTZ+RFgFwK3C3/fhu4OeLnHMzcJ8xZtgYMwLcB9wCYIx5whizqpOfE65F\nYAtBkYCxkzUU9AhBfivqsViKuohlCjrnnByeZjqZ0WIyRVkAGxsi/PPtV/Drr99Z9nuu29PKRDzN\nAXtCWv5shz3tUXdojzdryO8TGiJBhqeT/OhFa0v6X+++hE0NkQKrwMkEbPXc4NWGgzzxhzfwh28+\np+i6nPYeTi3BT4+PcOm2Rt560UaODU7ltNGOpzJuIarDga5ROpurly1+tFghaPds5KeB9iLnbAK6\nPM+77WNnBE6w2LEIivURmokRyIwQeM5LpDPEU1nXImisDiECL9u+v9UYUako64Gr7TnG5XLNrhZ8\nAg+/at3FnxiaJloVcBM4drfVulXG+Zuq1W8oxY9eOM3ZG2o5a0MtH3rDrgKrYMDTXsJLXThY0nKp\nj1hzD44PTTEWS/HK6XEu62zipr0b8An80HZFJdIZ3vnFx3nz5x7N6bx6oHt0WWeOzykEInK/iLxY\n5N+t3vOMJZnLVvEgIh8Ukf0isn9goHhPkYWQ7xoqVl3siEUo4KPB3uy9QuDEFRwhcO4uXjltVTGu\nVgtqRak06quDXLSlgYftNNLXBqfobKl2N2gnYAy5nV/BshBePT3BUyeG3Qrmt1+yucAq6PdUFc+H\nzuZqjg9O8/SJYYyBy7c30VpbxeXbm1wr5C9++Aov9IwxOJng/pesQHXfeJzesfiyThmcUwiMMTca\nY84r8u/7QJ+IdADY/y0WYu8BvGWJm+1j88IYc6cxZp8xZl9ra+k+5vPFcQ1tqA8jAoNFagQciyDk\n9xHw+6iPBHNiBDPtJXLT0Y7ZrWm1mExRVo7r9rTyfPcow1NJO3V0pl2Hk0IK5GQNgWURHOmfxBh4\n8/lWzUIo4HOtAieddMY1NF8hqOHE0BRPvjZM0C9cbGeWvfn8Dg71TfL5Bw/zfx87zn95XScb68N8\nc7/lSDnQZbm5VtUimIN7ACcL6Dbg+0XOuRe4SUQa7SDxTfaxMwLnbr+mKkBjdahoLYHrGrLH0nkr\nh6G4EDTXVLkmqMYIFGXluG5PK8bAw4f66RqJ0enpWNpqZw5BYWW3U1ewqy3Kbo/l8AsXb6KxOsg/\nPXECsGoIGqqDZbUm97KtuYZTY3EePTzI+ZvqXZfXzeduQAT++j8OceHmev7wzefwjn1b+MnhAbpH\npjnQPUrAJ+7An+VgsULwKeCNInIYuNF+jojsE5EvARhjhoE/BZ6y/33CPoaIfFpEuoFqEekWkf+5\nyPXMG8c1VBWwisUGJ2a3CKCwd/m4p/Oog/NHFgn6c/KJFUVZXi7c3EB9JMjXnjxppY56LAIRYU97\nLT6hoJ+TEzN4c16DvXDQzzv3beE/XuqjbzxeUFVcLp329MKDp8a5fHuze7y9LsxlnU3UhgN8/pcv\nIRTw8U67buLbT3dzoGuMsztql3XK4KJ2KGPMEHBDkeP7gds9z78MfLnIeb8P/P5i1rBYHIugKuCn\nOVrcIkh6KovBMim7R2aqBMfjRYTATiFt16piRVlR/D7hmt0t/OB5y++e38n1wi0NVtO4vKK+1qi1\nuTtuIS+/fPlW7nzkGF//6Um7qnj+7l6vIF2+Pbcu4u/edRHTyYw7w2JLUzVX72zhX/Z3Mx5P8dYL\nN877evNBK4tTHosgWlW0A2nSaToXsP5wmvMsgrEiFoGTpaDxAUVZeV6/eyaO6N2AAX73prP4zm+8\nruA977h0M//wvkuL9oDqbKnhuj2tfP2nJzk9tkCLwF6HCFy6rSnntY76CDtbc2ct/OJlW+gZjTER\nTy9rfABUCIg7FkHQR2u0quhwmlS+RVATYmQq5WYROGMqvaam4xrSjCFFWXmutdtN1IT8tERzYwGR\nkL9ooLehOsTN55ZubPfeK7bSN56gdyw+70AxWBlNDdVBzt5QV9asiJv2trvnXaRCsLw4WUMhu6Hc\neLywoZy3xQRYaWfJTNYdgjEWSxEJ+t3XrXMc15BaBIqy0nTURzirvZbtrTVL5pp9w9ltdNhtxhci\nBGCNDv21a7eXdW446Oddl22htbaqwFpYaio+iplIZwjYPcWbbR/h0FQiZ0art+kczPRAOTYwxYVb\nGnL6DDk0243nNGNIUVaHz/7SRWSXcJhLwO/j3Zdv5W/uO7Rgl++Hb9g9r/N/7+az+G8/sytnpOly\noEKQzrrReLdH0GQyRwjyg8Wv29mMCDxyaIALtzTk9Bly2FBvCYDTukJRlJVlOQb8vPfKbRzpn+TK\nHU1zn7wEWHVLy++4UddQOkOV7dJpsS2C/DiB02vIOa85WsX5m+rdsnNvnyGHXW21/J9fvWxWn6Oi\nKGuLppoQn3v3xeuukaQKQSrrEYIZi8BLvkUA8Po9rTxzcoSx6RRjsXTR4M/PnNWW8x5FUZQzkYrf\npRLpLFWua2gmRuAllcniE3L8dK/f00rWwH8eHWQ8lsqpKlYURVlLqBB4XEM1IT9VAV9Bv6FUJltw\nZ3/RlgZqwwEefnUgZ16xoijKWkOFID3jGhIRWorUEiQz2ZzUULCCONfubuGhV/uZSKQLytUVRVHW\nChUvBPFUJqd5VEs0VDRGECri63/9nlb67d7kahEoirJWqXghsGIEMz+G5mhV0RhBsaDvdXtmythV\nCBRFWauoEHiyhsBKD8u3CFIZU+AaAquw7OwNVrtaFQJFUdYqKgTpXNdQtCrAdDKTc04ynSXoL17Z\n93rbKtCsIUVR1ioqBHmuoXDQTyyVJwQlXEMAb71oIztaa9jZWlP0dUVRlDMdbTGRzuZYBJGgn2Q6\nSyZr3LqBVJGsIYdzN9bz4O9cvxJLVRRFWRbUIkhlcmIEkZD1OO6xClKZ4llDiqIo64GK393yXUMR\nu8rY6x6yYgQV/6NSFGWdUlG726unJzg6MOk+N8YUuIacTqQxT8A4mTHu4HpFUZT1RkXtbr/5tWf4\nzH+86j53Bs54XUPVISts4rUIUiUKyhRFUdYDFbW7RasCTMTT7vN4qlAInBiB1yKwgsU6gF5RlPVJ\nRQlBbTjgjpcEq4YAcLuPgsc15I0RzJI+qiiKstapqN2tNpxrESSKWQRFhCClwWJFUdYxFbW7RasC\nTHqFIF3MNWQJQTwvWFyqjkBRFGWtU1G7W204WNw1lFdQBuS0mUimMxosVhRl3VJRu1u0yooRZLLW\nDGLHIgjPUUeQypiSvYYURVHWOosSAhFpEpH7ROSw/d/GEufdZp9zWERus49Vi8gPROQVETkoIp9a\nzFrKoTZspYZOJS2rYCZG4LEIHNdQfmWxuoYURVmnLHZ3uwN4wBizG3jAfp6DiDQBHweuAC4HPu4R\njL82xpwNXAxcLSJv14mpkQAADyxJREFUWuR6ZsURAidgPJM1lNt0DmbSR7NZQzprNFisKMq6ZbG7\n263A3fbju4GfL3LOzcB9xphhY8wIcB9wizFm2hjzEIAxJgk8A2xe5HpmJVpltYqedIWgMFgc9PsI\n+sV1DTlFZyoEiqKsVxa7u7UbY3rtx6eB9iLnbAK6PM+77WMuItIA/ByWVVEUEfmgiOwXkf0DAwML\nWqxjEUwmUoBXCPw553lbUadsIdBgsaIo65U521CLyP3AhiIv/ZH3iTHGiIiZ7wJEJAB8HficMeZY\nqfOMMXcCdwLs27dv3tcBiNpCMG5bBE4coCrP/x8J+l3XUCpjXUpjBIqirFfmFAJjzI2lXhORPhHp\nMMb0ikgH0F/ktB7ges/zzcCPPc/vBA4bY/62rBUvgtoq2yLIdw0F84QgNGMRJNPqGlIUZX2z2N3t\nHuA2+/FtwPeLnHMvcJOINNpB4pvsY4jInwH1wH9f5DrKojZsxwgSTtZQYR0B5FsEjhBo+qiiKOuT\nxQrBp4A3ishh4Eb7OSKyT0S+BGCMGQb+FHjK/vcJY8ywiGzGci/tBZ4RkedE5PZFrmdWom7WUH6M\nYBaLwIkRqGtIUZR1yqJGVRpjhoAbihzfD9zuef5l4Mt553QDK3qbXRPyIzJ71hBYFkFcg8WKolQI\nFbW7iQjRqoAbLE6krTGVIrl6FAn63RYTGiNQFGW9U3G7W21VwBMjyBZYAwDhUGH6qE4oUxRlvVJx\nu1ttOJjjGvLOInCIBP1u99Fk2k4fVYtAUZR1SsXtbtFwgAm3oCxT1CKoLmIR6IQyRVHWK5UnBJ6Z\nBKVcQ5Gg1hEoilI5VNzuVhsOMJHwBosLXUPhoJ94Kks2azwWQcX9qBRFqRAqbnfzjqu0YgRFLAKn\nFXU6o03nFEVZ91Tc7lauawisVtSOa0iDxYqirFcqbnerDQeJpTKkM9mSriHvlDKn6ZxaBIqirFcq\nbneLOo3nEmnLNVTMIvBMKdMYgaIo652K2928U8pmqyMAiCWz2nROUZR1T2ULQSpDeBaLIJbKuP2I\n1DWkKMp6peJ2N3dcpeMaKpI15Mwtnk6mtemcoijrnorb3bzjKq0YQWnXkBMjCPgEn09dQ4qirE8q\nTgiiHtdQPFW8xYTXNZRMZ9UtpCjKuqbidjhnXOXodIp01hS1CKpD3mCx0UCxoijrmsoTAntc5dBU\nEiicVwwzMYJYyqosDhURC0VRlPVCxQlBOOjD7xMGJxNA4XQyyIsRpLOE1CJQFGUdU3FCICLUhgMM\nuUJQeLcf9At+nzCdTJPMZHUojaIo65qK3OGiVQEGJ23XUJFNXkSsVtR2QZmmjiqKsp6pyB0uWuWx\nCIrECMCKE1hZQ0azhhRFWddU5A5XFw4yZFsE4RKB4OqQ360jUNeQoijrmYrc4aKe4TSlLALLNWTV\nEWiwWFGU9UxlCoFdSwDFg8UA4ZCfadsi0M6jiqKsZypyh3PaTEDxYDFAJOgjnrRdQxojUBRlHbOo\nHU5EmkTkPhE5bP+3scR5t9nnHBaR2zzH/11EDojIQRH5ooisSOVW1CsEs7mG7O6jKgSKoqxnFrvD\n3QE8YIzZDTxgP89BRJqAjwNXAJcDH/cIxi8aYy4EzgNagXcucj1lUWdXF0Np11Ak5LcnlKlrSFGU\n9c1id7hbgbvtx3cDP1/knJuB+4wxw8aYEeA+4BYAY8y4fU4ACAFmkespi9wYQSmLIEAsaY2q1DoC\nRVHWM4vd4dqNMb3249NAe5FzNgFdnufd9jEAROReoB+YAL69yPWURVlCEPLNpI9q1pCiKOuYwFwn\niMj9wIYiL/2R94kxxojIvO/ojTE3i0gY+GfgDVgWQ7F1fBD4IMDWrVvne5kccoLFRUZVghUjmE5m\nAJ1OpijK+mZOITDG3FjqNRHpE5EOY0yviHRg3dnn0wNc73m+Gfhx3jXiIvJ9LFdTUSEwxtwJ3Amw\nb9++RbmQomVlDVkxgoBPNEagKMq6ZrE73D2AkwV0G/D9IufcC9wkIo12kPgm4F4RidrigYgEgJ8F\nXlnkesqi1h5X6fdJybv9sD2TYDKZ1hiBoijrmsXucJ8C3igih4Eb7eeIyD4R+RKAMWYY+FPgKfvf\nJ+xjNcA9IvI88ByWNfHFRa6nLBzXUClrAGZaURujriFFUdY3c7qGZsMYMwTcUOT4fuB2z/MvA1/O\nO6cPuGwx118o0XkIAaCuIUVR1jUVucM5WU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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]}]}
\ No newline at end of file
%% Cell type:code id: tags:
```
import torch
import torch.nn as nn
import numpy as np
from torchvision import datasets, transforms
import seaborn as sns
import matplotlib.pyplot as plt
GPU = True
device_idx = 0
if GPU:
device = torch.device("cuda:" + str(device_idx) if torch.cuda.is_available() else "cpu")