CH 7.2 - 케라스 활용법.ipynb

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      "Epoch 1/10\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 0.7079 - accuracy: 0.5060\n",
      "Epoch 2/10\n",
      "32/32 [==============================] - 0s 8ms/step - loss: 0.6980 - accuracy: 0.5150\n",
      "Epoch 3/10\n",
      "32/32 [==============================] - 0s 8ms/step - loss: 0.6935 - accuracy: 0.5370\n",
      "Epoch 4/10\n",
      "32/32 [==============================] - 0s 11ms/step - loss: 0.6910 - accuracy: 0.5430\n",
      "Epoch 5/10\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.6879 - accuracy: 0.5410\n",
      "Epoch 6/10\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 0.6859 - accuracy: 0.5610\n",
      "Epoch 7/10\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 0.6811 - accuracy: 0.5570\n",
      "Epoch 8/10\n",
      "32/32 [==============================] - 0s 9ms/step - loss: 0.6806 - accuracy: 0.5830\n",
      "Epoch 9/10\n",
      "32/32 [==============================] - 0s 7ms/step - loss: 0.6749 - accuracy: 0.5740\n",
      "Epoch 10/10\n",
      "32/32 [==============================] - 0s 10ms/step - loss: 0.6738 - accuracy: 0.5770\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "data = np.random.random((1000,100))\n",
    "labels = np.random.randint(2, size = (1000, 1))\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(32, activation = 'relu', input_dim=100))\n",
    "model.add(Dense(1, activation = 'sigmoid'))\n",
    "\n",
    "model.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "\n",
    "history = model.fit(data, labels, epochs = 10, batch_size = 32)\n",
    "\n",
    "predictions = model.predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=8, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "model.add(Dense(8, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "model.add(Dense(1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
    "\n",
    "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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