Keras Ch 7 Callbacks and Tensorboard.ipynb

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      "loading data\n",
      "Pad Sequences (samples x time)\n",
      "('x_train.shape:', (25000, 500))\n",
      "('x_test.shape:', (25000, 500))\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 500, 128)          256000    \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 494, 32)           28704     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 98, 32)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 85, 32)            14368     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_1 (Glob (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 299,105\n",
      "Trainable params: 299,105\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from keras.datasets import imdb\n",
    "from keras.preprocessing import sequence\n",
    "\n",
    "max_features = 2000\n",
    "max_len = 500\n",
    "\n",
    "print('loading data')\n",
    "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\n",
    "\n",
    "print('Pad Sequences (samples x time)')\n",
    "x_train = sequence.pad_sequences(x_train, maxlen=max_len)\n",
    "x_test = sequence.pad_sequences(x_test, maxlen=max_len)\n",
    "print('x_train.shape:', x_train.shape)\n",
    "print('x_test.shape:', x_test.shape)\n",
    "\n",
    "\n",
    "from keras import models\n",
    "from keras import layers\n",
    "from keras.optimizers import RMSprop\n",
    "\n",
    "from util import print_curves\n",
    "\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "\n",
    "def plot_model_jupyter(model):\n",
    "    SVG(model_to_dot(model).create(prog='dot', format='svg'))\n",
    "\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Embedding(max_features, 128, input_length=max_len))\n",
    "model.add(layers.Conv1D(32, 7, activation='relu'))\n",
    "model.add(layers.MaxPooling1D(5))\n",
    "model.add(layers.Conv1D(32, 14, activation='relu'))\n",
    "# default pool size 2\n",
    "model.add(layers.GlobalMaxPooling1D())\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()\n",
    "plot_model_jupyter(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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     "text": [
      "Train on 20000 samples, validate on 5000 samples\n",
      "Epoch 1/20\n",
      "20000/20000 [==============================] - 13s 662us/step - loss: 0.5107 - acc: 0.7276 - val_loss: 0.3525 - val_acc: 0.8492\n",
      "Epoch 2/20\n",
      "20000/20000 [==============================] - 13s 658us/step - loss: 0.3147 - acc: 0.8652 - val_loss: 0.3092 - val_acc: 0.8678\n",
      "Epoch 3/20\n",
      "20000/20000 [==============================] - 13s 649us/step - loss: 0.2738 - acc: 0.8860 - val_loss: 0.3753 - val_acc: 0.8430\n",
      "Epoch 4/20\n",
      "20000/20000 [==============================] - 12s 614us/step - loss: 0.2444 - acc: 0.9016 - val_loss: 0.3373 - val_acc: 0.8620\n",
      "Epoch 5/20\n",
      "20000/20000 [==============================] - 12s 621us/step - loss: 0.2136 - acc: 0.9167 - val_loss: 0.3206 - val_acc: 0.8724\n",
      "Epoch 6/20\n",
      "20000/20000 [==============================] - 12s 621us/step - loss: 0.1804 - acc: 0.9306 - val_loss: 0.3162 - val_acc: 0.8720\n",
      "Epoch 7/20\n",
      "20000/20000 [==============================] - 12s 624us/step - loss: 0.1464 - acc: 0.9482 - val_loss: 0.4249 - val_acc: 0.8482\n",
      "Epoch 8/20\n",
      "20000/20000 [==============================] - 12s 621us/step - loss: 0.1142 - acc: 0.9619 - val_loss: 0.4356 - val_acc: 0.8458\n",
      "Epoch 9/20\n",
      "20000/20000 [==============================] - 12s 623us/step - loss: 0.0835 - acc: 0.9745 - val_loss: 0.4148 - val_acc: 0.8572\n",
      "Epoch 10/20\n",
      "20000/20000 [==============================] - 12s 620us/step - loss: 0.0558 - acc: 0.9843 - val_loss: 0.4239 - val_acc: 0.8662\n",
      "Epoch 11/20\n",
      "20000/20000 [==============================] - 12s 619us/step - loss: 0.0364 - acc: 0.9914 - val_loss: 0.4698 - val_acc: 0.8648\n",
      "Epoch 12/20\n",
      "20000/20000 [==============================] - 12s 616us/step - loss: 0.0217 - acc: 0.9952 - val_loss: 0.6483 - val_acc: 0.8462\n",
      "Epoch 13/20\n",
      "20000/20000 [==============================] - 12s 623us/step - loss: 0.0154 - acc: 0.9955 - val_loss: 0.5559 - val_acc: 0.8620\n",
      "Epoch 14/20\n",
      "20000/20000 [==============================] - 12s 624us/step - loss: 0.0083 - acc: 0.9981 - val_loss: 0.6264 - val_acc: 0.8624\n",
      "Epoch 15/20\n",
      "20000/20000 [==============================] - 12s 623us/step - loss: 0.0066 - acc: 0.9982 - val_loss: 0.6795 - val_acc: 0.8642\n",
      "Epoch 16/20\n",
      "20000/20000 [==============================] - 12s 624us/step - loss: 0.0046 - acc: 0.9987 - val_loss: 0.7069 - val_acc: 0.8616\n",
      "Epoch 17/20\n",
      "20000/20000 [==============================] - 12s 621us/step - loss: 0.0028 - acc: 0.9991 - val_loss: 0.8664 - val_acc: 0.8518\n",
      "Epoch 18/20\n",
      "20000/20000 [==============================] - 12s 617us/step - loss: 0.0028 - acc: 0.9990 - val_loss: 0.7870 - val_acc: 0.8644\n",
      "Epoch 19/20\n",
      "20000/20000 [==============================] - 12s 619us/step - loss: 0.0014 - acc: 0.9996 - val_loss: 0.8363 - val_acc: 0.8578\n",
      "Epoch 20/20\n",
      "20000/20000 [==============================] - 13s 632us/step - loss: 3.5130e-04 - acc: 1.0000 - val_loss: 0.8764 - val_acc: 0.8598\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "\n",
    "model.compile(optimizer='rmsprop',loss='binary_crossentropy', metrics=['acc'])\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.TensorBoard(\n",
    "    log_dir='my_log_dir',\n",
    "    histogram_freq=1,\n",
    "    embeddings_freq=1)\n",
    "]\n",
    "\n",
    "history = model.fit(x_train, y_train,\n",
    "                   epochs=20,\n",
    "                   batch_size=128,\n",
    "                   validation_split=0.2,\n",
    "                   callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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