8.1.API-funcional-de-Keras.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"8.1.FunctionalAPI.ipynb","version":"0.3.2","provenance":[{"file_id":"https://github.com/tensorflow/docs/blob/master/site/en/r2/guide/keras/functional.ipynb","timestamp":1565972442929}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"colab_type":"text","id":"-zMKQx6DkKwt"},"source":["## 8.1 API Funcional\n","### Deep Learning, Introducción práctica con Keras - Segunda parte"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"HFbM9dcfxh4l","outputId":"01417a52-22a3-4683-acc6-4bd95bdad320","executionInfo":{"status":"ok","timestamp":1566981858048,"user_tz":-120,"elapsed":6604,"user":{"displayName":"Jordi Torres","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDkD6udBQxc1Cx-zMrYPf0xjtQYxGjQEpB40wVHTg=s64","userId":"14559872433417615139"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["from __future__ import absolute_import, division, print_function, unicode_literals\n","\n","try:\n","  # %tensorflow_version only exists in Colab.\n","  %tensorflow_version 2.x\n","except Exception:\n","  pass\n","import tensorflow as tf\n","\n","tf.keras.backend.clear_session()  # For easy reset of notebook state."],"execution_count":1,"outputs":[{"output_type":"stream","text":["TensorFlow 2.x selected.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"ZI47-lpfkZ5c"},"source":["\n","Modelo base:\n","\n"]},{"cell_type":"code","metadata":{"id":"i5RjnmRVnW-3","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":255},"outputId":"2ac2dcdf-46e0-49ff-a223-ee5e55c53335","executionInfo":{"status":"ok","timestamp":1566981914369,"user_tz":-120,"elapsed":1051,"user":{"displayName":"Jordi Torres","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDkD6udBQxc1Cx-zMrYPf0xjtQYxGjQEpB40wVHTg=s64","userId":"14559872433417615139"}}},"source":["\n","mnist = tf.keras.datasets.mnist\n","\n","(x_train, y_train), (x_test, y_test) = mnist.load_data()\n","x_train = x_train.reshape(60000, 784).astype('float32') / 255\n","x_test = x_test.reshape(10000, 784).astype('float32') / 255\n","\n","model_base = tf.keras.models.Sequential([\n","  tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),\n","  tf.keras.layers.Dense(64, activation='relu'),\n","  tf.keras.layers.Dense(10, activation='softmax')\n","])\n","\n","model_base.summary()\n","\n"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Model: \"sequential_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","dense_3 (Dense)              (None, 64)                50240     \n","_________________________________________________________________\n","dense_4 (Dense)              (None, 64)                4160      \n","_________________________________________________________________\n","dense_5 (Dense)              (None, 10)                650       \n","=================================================================\n","Total params: 55,050\n","Trainable params: 55,050\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"eUwtA0LIvCS3","colab_type":"text"},"source":["Modelo con la *functional API* de Keras:\n"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"Yxi0LaSHkDT-","colab":{}},"source":["from tensorflow import keras\n","\n","inputs  = keras.Input(shape=(784,))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"ddIr9LPJnibj","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"7dc9be5c-639c-4149-bad7-4a7c22e496db","executionInfo":{"status":"ok","timestamp":1566981930906,"user_tz":-120,"elapsed":537,"user":{"displayName":"Jordi Torres","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDkD6udBQxc1Cx-zMrYPf0xjtQYxGjQEpB40wVHTg=s64","userId":"14559872433417615139"}}},"source":["inputs.shape"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["TensorShape([None, 784])"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"colab_type":"code","id":"lZkLJeQonmTe","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"11f76615-0552-419b-e65e-0331380483c9","executionInfo":{"status":"ok","timestamp":1566981933130,"user_tz":-120,"elapsed":543,"user":{"displayName":"Jordi Torres","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mDkD6udBQxc1Cx-zMrYPf0xjtQYxGjQEpB40wVHTg=s64","userId":"14559872433417615139"}}},"source":["inputs.dtype"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tf.float32"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab_type":"code","id":"sMyyMTqDnpYV","colab":{}},"source":["from tensorflow.keras import layers\n","\n","dense1  = layers.Dense(64, activation='relu')(inputs)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"DbF-MIO2ntf7","colab":{}},"source":["dense2  = layers.Dense(64, activation='relu')(dense1)\n","outputs = layers.Dense(10, activation='softmax')(dense2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"MrSfwvl-nx9s","colab":{}},"source":["model = keras.Model(inputs=inputs, outputs=outputs)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Y7_p0S33EKl-","colab_type":"code","outputId":"e34edfb6-aee5-4969-ff7a-b850e07d3fb7","executionInfo":{"status":"ok","timestamp":1565986445146,"user_tz":-120,"elapsed":576,"user":{"displayName":"Jordi Torres","photoUrl":"https://lh4.googleusercontent.com/-SfQ_VFnu6kE/AAAAAAAAAAI/AAAAAAAABzE/lQO8NYx2THo/s64/photo.jpg","userId":"14559872433417615139"}},"colab":{"base_uri":"https://localhost:8080/","height":289}},"source":["model.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model: \"model\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","input_1 (InputLayer)         [(None, 784)]             0         \n","_________________________________________________________________\n","dense_4 (Dense)              (None, 64)                50240     \n","_________________________________________________________________\n","dense_5 (Dense)              (None, 64)                4160      \n","_________________________________________________________________\n","dense_6 (Dense)              (None, 10)                650       \n","=================================================================\n","Total params: 55,050\n","Trainable params: 55,050\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"5EeeV1xJn3jW"},"source":["Resumen del modelo:"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"xkz7oqj2n1-q","colab":{}},"source":["inputs  = keras.Input(shape=(784,))\n","dense1  = layers.Dense(64, activation='relu')(inputs)\n","dense2  = layers.Dense(64, activation='relu')(dense1)\n","outputs = layers.Dense(10, activation='softmax')(dense2)\n","\n","model = keras.Model(inputs=inputs, outputs=outputs, name='mnist_model')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"GirC9odQn5Ep","colab":{}},"source":["model.summary()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"7FGesSSuoAG5","colab":{}},"source":["keras.utils.plot_model(model, 'model.png', show_shapes=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"m-DS8N7Nx23l","colab_type":"code","colab":{}},"source":["model.compile(optimizer='adam',\n","       loss='sparse_categorical_crossentropy',\n","       metrics=['accuracy'])\n","\n","model.fit(x_train, y_train, epochs=5)\n","\n","model.evaluate(x_test, y_test)\n"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"lKz1WWr2LUzF"},"source":["Diferentes opciones de uso de la API\n"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"ZBlZbRn5uk-9","outputId":"c1346e20-9878-4d3b-aefa-1f2a7eeef21f","executionInfo":{"status":"ok","timestamp":1565988197937,"user_tz":-120,"elapsed":668,"user":{"displayName":"Jordi Torres","photoUrl":"https://lh4.googleusercontent.com/-SfQ_VFnu6kE/AAAAAAAAAAI/AAAAAAAABzE/lQO8NYx2THo/s64/photo.jpg","userId":"14559872433417615139"}},"colab":{"base_uri":"https://localhost:8080/","height":357}},"source":["def get_model():\n","  inputs = keras.Input(shape=(128,))\n","  outputs = layers.Dense(1, activation='sigmoid')(inputs)\n","  return keras.Model(inputs, outputs)\n","\n","model1 = get_model()\n","model2 = get_model()\n","model3 = get_model()\n","\n","inputs = keras.Input(shape=(128,))\n","y1 = model1(inputs)\n","y2 = model2(inputs)\n","y3 = model3(inputs)\n","outputs = layers.average([y1, y2, y3])\n","ensemble_model = keras.Model(inputs=inputs, outputs=outputs)\n","\n","ensemble_model.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model: \"model_3\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_5 (InputLayer)            [(None, 128)]        0                                            \n","__________________________________________________________________________________________________\n","model (Model)                   (None, 1)            129         input_5[0][0]                    \n","__________________________________________________________________________________________________\n","model_1 (Model)                 (None, 1)            129         input_5[0][0]                    \n","__________________________________________________________________________________________________\n","model_2 (Model)                 (None, 1)            129         input_5[0][0]                    \n","__________________________________________________________________________________________________\n","average (Average)               (None, 1)            0           model[1][0]                      \n","                                                                 model_1[1][0]                    \n","                                                                 model_2[1][0]                    \n","==================================================================================================\n","Total params: 387\n","Trainable params: 387\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ZlLIORIYI9U1","colab_type":"code","outputId":"ef898f8b-99c1-4108-e33e-e793c11cf34f","executionInfo":{"status":"ok","timestamp":1565988369053,"user_tz":-120,"elapsed":744,"user":{"displayName":"Jordi Torres","photoUrl":"https://lh4.googleusercontent.com/-SfQ_VFnu6kE/AAAAAAAAAAI/AAAAAAAABzE/lQO8NYx2THo/s64/photo.jpg","userId":"14559872433417615139"}},"colab":{"base_uri":"https://localhost:8080/","height":352}},"source":["keras.utils.plot_model(ensemble_model, show_shapes=True)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABC8AAAEnCAYAAACJ2mn3AAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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2rQJKSkp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RWq/sw19TUNHufsWPHYunSpcjMzMTGjRvVtllZWQFAgwmyJffM1tYWABAWFgZBENSWs2fP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\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"colab_type":"code","id":"R9pAPQCnKuMR","outputId":"57c1e2f2-9ba6-493d-f0e1-a30c152c7187","executionInfo":{"status":"ok","timestamp":1565990856061,"user_tz":-120,"elapsed":2030,"user":{"displayName":"Jordi Torres","photoUrl":"https://lh4.googleusercontent.com/-SfQ_VFnu6kE/AAAAAAAAAAI/AAAAAAAABzE/lQO8NYx2THo/s64/photo.jpg","userId":"14559872433417615139"}},"colab":{"base_uri":"https://localhost:8080/","height":573}},"source":["num_tags = 12  \n","num_words = 10000  \n","num_departments = 4  \n","\n","title_input = keras.Input(shape=(None,), name='title') \n","body_input = keras.Input(shape=(None,), name='body')  \n","tags_input = keras.Input(shape=(num_tags,), name='tags')  \n","\n","title_features = layers.Embedding(num_words, 64)(title_input)\n","body_features = layers.Embedding(num_words, 64)(body_input)\n","\n","title_features = layers.LSTM(128)(title_features)\n","body_features = layers.LSTM(32)(body_features)\n","\n","x = layers.concatenate([title_features, body_features, tags_input])\n","\n","priority_pred = layers.Dense(1, activation='sigmoid', name='priority')(x)\n","department_pred = layers.Dense(num_departments, activation='softmax')(x)\n","\n","model = keras.Model(inputs=[title_input, body_input, tags_input],\n","                    outputs=[priority_pred, department_pred])\n","\n","keras.utils.plot_model(model, show_shapes=True)\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABOsAAAIECAIAAADHPD1+AAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\nne/cudPT03P9+vU5c+YIhcJbt24xo7GxsXw+/8iRI42NjaWlpbNmzbKysn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RYsaGxu1WCxAf0g51pNEl2tjIOVAlxlwiFlaWubn51dUVKSnpysW6kWsMSorK3/1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\njtf+85//HDZs2KZNmwwMDFxdXdev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eXl5yg38v4hEokOHDnE4HKnlu3btcnZ2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