demo_imlp.ipynb

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     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from imlp import iAct, iLoss, get_model\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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     "text": [
      "Epoch 1/10\n",
      "100/100 [==============================] - 1s 10ms/step - loss: 3.5982 - dense_17_loss: 2.6060 - dense_18_loss: 0.0421 - dense_17_accuracy: 0.0000e+00 - dense_18_accuracy: 1.0000\n",
      "Epoch 2/10\n",
      "100/100 [==============================] - 0s 160us/step - loss: 2.1682 - dense_17_loss: 1.2155 - dense_18_loss: 0.0219 - dense_17_accuracy: 0.0000e+00 - dense_18_accuracy: 1.0000\n",
      "Epoch 3/10\n",
      "100/100 [==============================] - 0s 162us/step - loss: 1.0881 - dense_17_loss: 0.2661 - dense_18_loss: 0.0198 - dense_17_accuracy: 0.0000e+00 - dense_18_accuracy: 1.0000\n",
      "Epoch 4/10\n",
      "100/100 [==============================] - 0s 164us/step - loss: 1.0417 - dense_17_loss: 0.2098 - dense_18_loss: 0.0226 - dense_17_accuracy: 0.0000e+00 - dense_18_accuracy: 1.0000\n",
      "Epoch 5/10\n",
      "100/100 [==============================] - 0s 160us/step - loss: 1.2980 - dense_17_loss: 0.6110 - dense_18_loss: 0.0042 - dense_17_accuracy: 0.0000e+00 - dense_18_accuracy: 1.0000\n",
      "Epoch 6/10\n",
      "100/100 [==============================] - 0s 155us/step - loss: 1.0341 - dense_17_loss: 0.3759 - dense_18_loss: 0.0045 - dense_17_accuracy: 0.0000e+00 - dense_18_accuracy: 1.0000\n",
      "Epoch 7/10\n",
      "100/100 [==============================] - 0s 154us/step - loss: 1.0096 - dense_17_loss: 0.2187 - dense_18_loss: 0.0030 - dense_17_accuracy: 0.0000e+00 - dense_18_accuracy: 1.0000\n",
      "Epoch 8/10\n",
      "100/100 [==============================] - 0s 146us/step - loss: 0.9157 - dense_17_loss: 0.1295 - dense_18_loss: 0.0011 - dense_17_accuracy: 0.0000e+00 - dense_18_accuracy: 1.0000\n",
      "Epoch 9/10\n",
      "100/100 [==============================] - 0s 153us/step - loss: 0.8316 - dense_17_loss: 0.0787 - dense_18_loss: 2.2645e-04 - dense_17_accuracy: 0.0000e+00 - dense_18_accuracy: 1.0000\n",
      "Epoch 10/10\n",
      "100/100 [==============================] - 0s 141us/step - loss: 0.8235 - dense_17_loss: 0.0443 - dense_18_loss: 1.1491e-04 - dense_17_accuracy: 0.0000e+00 - dense_18_accuracy: 1.0000\n"
     ]
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       "<keras.callbacks.callbacks.History at 0x645810978>"
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     "execution_count": 4,
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   "source": [
    "# Generate the synthetic data\n",
    "x1 = np.sin(np.arange(0, 9, 0.01))\n",
    "x2 = np.cos(np.arange(0, 9, 0.01))\n",
    "x3 = x1**2\n",
    "x4 = (x1+x2)/2\n",
    "tmp = np.ones((900,))\n",
    "\n",
    "Xtrain_c = x3[0:5]\n",
    "Xtrain_r = tmp[0:5]\n",
    "Ytrain_c = x4[0:1]\n",
    "Ytrain_r = tmp[0:1]\n",
    "\n",
    "for i in range(1,100):\n",
    "    Xtrain_c = np.vstack((Xtrain_c, x3[i:i+5]))\n",
    "    Xtrain_r = np.vstack((Xtrain_r, tmp[i:i+5]))\n",
    "    Ytrain_c = np.vstack((Ytrain_c, x4[i:i+1]))\n",
    "    Ytrain_r = np.vstack((Ytrain_r, tmp[i:i+1]))\n",
    "\n",
    "# Parameters\n",
    "input_dim = 5\n",
    "output_dim = 1\n",
    "num_hidden_layers = 2\n",
    "num_units = [200, 200]\n",
    "act = ['relu', 'relu']\n",
    "beta = 0.5\n",
    "\n",
    "# Get model\n",
    "model = get_model(input_dim, output_dim, num_units, act, beta, num_hidden_layers)\n",
    "\n",
    "# Train\n",
    "model.fit(x=[Xtrain_c, Xtrain_r], y=[Ytrain_c, Ytrain_r], epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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