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@gokceneraslan
Last active November 4, 2017 22:35
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/gokcen/.miniconda3/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n",
"Using TensorFlow backend.\n"
]
},
{
"data": {
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SA150vu1O2ZcA/oo92DvbLFKl2GNb2kjOXlVNmmlisf9O3FmkOoCurRpXFgFH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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x125ceffd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<ggplot: (-9223372036546736924)>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import plotnine as p9\n",
"\n",
"import torch\n",
"from torch.autograd import Variable\n",
"from torch.utils.data import TensorDataset, DataLoader, Dataset\n",
"\n",
"np.random.seed(555)\n",
"torch.manual_seed(555)\n",
"\n",
"num_sample = 300\n",
"num_feat = 10\n",
"num_out = 10\n",
"\n",
"batch_size = 32\n",
"lr = 1e-4\n",
"epochs = 300\n",
"\n",
"X = np.random.normal(0, 0.5, (num_sample, num_feat)).astype(np.float64)\n",
"W = np.random.normal(0, 0.5, (num_feat, num_out)).astype(np.float64)\n",
"b = np.random.normal(0, 0.5, (1, num_out)).astype(np.float64)\n",
"Y = np.dot(X, W) + b\n",
"X -= X.mean(0)\n",
"\n",
"ds = TensorDataset(torch.from_numpy(X).double(), torch.from_numpy(Y).double())\n",
"\n",
"# train torch models with shuffling\n",
"model1 = torch.nn.Linear(num_feat, num_out).double()\n",
"opt1 = torch.optim.SGD(model1.parameters(), lr=lr)\n",
"loss1 = torch.nn.MSELoss()\n",
"\n",
"train_hist_torch = []\n",
"\n",
"for epoch in range(epochs):\n",
"\n",
" train_batch_losses = []\n",
" for x, y in DataLoader(ds, batch_size=batch_size, shuffle=True, drop_last=True):\n",
" x_var, y_var = Variable(x, requires_grad=False), Variable(y, requires_grad=False)\n",
" pred = model1(x_var)\n",
" l = loss1(pred, y_var)\n",
" train_batch_losses.append(l.data[0])\n",
"\n",
" opt1.zero_grad()\n",
" l.backward()\n",
" opt1.step()\n",
"\n",
" # save mean of all batch errors within the epoch\n",
" train_hist_torch.append(np.array(train_batch_losses).mean())\n",
"\n",
"\n",
"from keras.models import Model\n",
"from keras.layers import Input, Dense\n",
"from keras.optimizers import SGD\n",
"from keras import backend as K\n",
"\n",
"\n",
"inputs = Input(shape=(num_feat,))\n",
"predictions = Dense(num_out, activation='linear')(inputs)\n",
"model = Model(inputs=inputs, outputs=predictions)\n",
"\n",
"opt = SGD(lr=lr)\n",
"model.compile(optimizer=opt, loss='mse')\n",
"losses = model.fit(X, Y,\n",
" batch_size=batch_size,\n",
" epochs=epochs, verbose=0, shuffle=True)\n",
" \n",
"train_hist_keras = losses.history['loss']\n",
" \n",
"(p9.ggplot(pd.DataFrame({'torch_train_torch': train_hist_torch,\n",
" 'torch_train_keras': train_hist_keras,\n",
" 'epochs': range(len(train_hist_torch))}),\n",
" p9.aes(x='epochs')) +\n",
" p9.geom_path(p9.aes(y='torch_train_torch', color='\"training loss (sgd, torch)\"')) +\n",
" p9.geom_path(p9.aes(y='torch_train_keras', color='\"training loss (sgd, keras)\"')) +\n",
" p9.labs(color='Loss', y=' ') +\n",
" p9.theme_minimal())"
]
}
],
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