adding three jupyter notebook for 7.1.1, 7.1.2 and 7.1.3

7.1.1-introduction-to-funcational-API.ipynb

@@ -0,0 +1,117 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense (Dense)                (None, 32)                2080      \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 32)                1056      \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 10)                330       \n",
+      "=================================================================\n",
+      "Total params: 3,466\n",
+      "Trainable params: 3,466\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "Model: \"model\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_1 (InputLayer)         [(None, 64)]              0         \n",
+      "_________________________________________________________________\n",
+      "dense_3 (Dense)              (None, 32)                2080      \n",
+      "_________________________________________________________________\n",
+      "dense_4 (Dense)              (None, 32)                1056      \n",
+      "_________________________________________________________________\n",
+      "dense_5 (Dense)              (None, 10)                330       \n",
+      "=================================================================\n",
+      "Total params: 3,466\n",
+      "Trainable params: 3,466\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Sequential, Model \n",
+    "from keras import layers\n",
+    "from keras import Input\n",
+    "seq_model = Sequential()\n",
+    "seq_model.add(layers.Dense(32, activation='relu', input_shape=(64,))) \n",
+    "seq_model.add(layers.Dense(32, activation='relu')) \n",
+    "seq_model.add(layers.Dense(10, activation='softmax'))\n",
+    "seq_model.summary()\n",
+    "\n",
+    "input_tensor = Input(shape=(64,))\n",
+    "x = layers.Dense(32, activation='relu')(input_tensor)\n",
+    "x = layers.Dense(32, activation='relu')(x)\n",
+    "output_tensor = layers.Dense(10, activation='softmax')(x)\n",
+    "model = Model(input_tensor, output_tensor) \n",
+    "model.summary() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-3-233ca56a8921>, line 6)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-233ca56a8921>\"\u001b[0;36m, line \u001b[0;32m6\u001b[0m\n\u001b[0;31m    Generates dummy Numpy data to train on\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n",
+    "\n",
+    "import numpy as np\n",
+    "x_train = np.random.random((1000, 64)) \n",
+    "y_train = np.random.random((1000, 10))\n",
+    "\n",
+    "model.fit(x_train, y_train, epochs=10, batch_size=128)\n",
+    "score = model.evaluate(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

7.1.2-build-a-concatenate-network.ipynb

@@ -0,0 +1,181 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# two inputs, one output model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model_9\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "text (InputLayer)               [(None, None)]       0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "question (InputLayer)           [(None, None)]       0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "embedding_20 (Embedding)        (None, None, 64)     640000      text[0][0]                       \n",
+      "__________________________________________________________________________________________________\n",
+      "embedding_21 (Embedding)        (None, None, 32)     320000      question[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lstm_20 (LSTM)                  (None, 32)           12416       embedding_20[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "lstm_21 (LSTM)                  (None, 16)           3136        embedding_21[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_8 (Concatenate)     (None, 48)           0           lstm_20[0][0]                    \n",
+      "                                                                 lstm_21[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_8 (Dense)                 (None, 500)          24500       concatenate_8[0][0]              \n",
+      "==================================================================================================\n",
+      "Total params: 1,000,052\n",
+      "Trainable params: 1,000,052\n",
+      "Non-trainable params: 0\n",
+      "__________________________________________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.models import Model\n",
+    "from keras import layers\n",
+    "from keras import Input\n",
+    "\n",
+    "text_vocabulary_size     = 10000\n",
+    "question_vocabulary_size = 10000\n",
+    "answer_vocabulary_size = 500\n",
+    "\n",
+    "text_input = Input(shape=(None,), dtype = 'int32', name ='text')\n",
+    "embedded_text = layers.Embedding(text_vocabulary_size,64)(text_input)\n",
+    "encoded_text  = layers.LSTM(32)(embedded_text)\n",
+    "\n",
+    "question_input = Input(shape=(None,), dtype = 'int32', name = 'question')\n",
+    "embedded_question = layers.Embedding(question_vocabulary_size,32)(question_input)\n",
+    "encoded_question  = layers.LSTM(16)(embedded_question)\n",
+    "\n",
+    "# concatenate two inputs and give output\n",
+    "concatenated = layers.concatenate([encoded_text, encoded_question], axis=-1)\n",
+    "answer = layers.Dense(answer_vocabulary_size, activation='softmax')(concatenated)\n",
+    "\n",
+    "model = Model([text_input, question_input], answer)\n",
+    "\n",
+    "model.compile(optimizer = 'rmsprop', loss='categorical_crossentropy', metrics=['acc'])\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "8/8 [==============================] - 2s 37ms/step - loss: 1553.1508 - acc: 0.0012\n",
+      "Epoch 2/10\n",
+      "8/8 [==============================] - 0s 39ms/step - loss: 1556.4614 - acc: 0.0000e+00\n",
+      "Epoch 3/10\n",
+      "8/8 [==============================] - 0s 38ms/step - loss: 1562.3912 - acc: 0.0000e+00\n",
+      "Epoch 4/10\n",
+      "8/8 [==============================] - 0s 36ms/step - loss: 1568.0669 - acc: 0.0000e+00\n",
+      "Epoch 5/10\n",
+      "8/8 [==============================] - 0s 37ms/step - loss: 1570.9047 - acc: 0.0000e+00\n",
+      "Epoch 6/10\n",
+      "8/8 [==============================] - 0s 37ms/step - loss: 1569.9131 - acc: 0.0000e+00\n",
+      "Epoch 7/10\n",
+      "8/8 [==============================] - 0s 40ms/step - loss: 1575.4770 - acc: 0.0000e+00\n",
+      "Epoch 8/10\n",
+      "8/8 [==============================] - 0s 43ms/step - loss: 1572.6059 - acc: 0.0000e+00\n",
+      "Epoch 9/10\n",
+      "8/8 [==============================] - 0s 35ms/step - loss: 1572.1957 - acc: 0.0000e+00\n",
+      "Epoch 10/10\n",
+      "8/8 [==============================] - 0s 36ms/step - loss: 1572.1025 - acc: 0.0000e+00\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x7fc58901a430>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "num_samples = 1000\n",
+    "max_length = 100\n",
+    "\n",
+    "text     = np.random.randint(1, text_vocabulary_size, size = (num_samples, max_length))\n",
+    "\n",
+    "question = np.random.randint(1, question_vocabulary_size, size = (num_samples, max_length))\n",
+    "\n",
+    "answers  = np.random.randint(0,2, size = (num_samples, answer_vocabulary_size))\n",
+    "\n",
+    "\n",
+    "model.fit([text, question], answers, epochs = 10, batch_size = 128)\n",
+    "#model.fit({\"text\":text, \"question\":question}, answers, epochs=10, batch_size=128)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<KerasTensor: shape=(None, 1000, 100) dtype=int32 (created by layer 'text')>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}