Coursera Tensorflow Developer Professional Certificate - intro tensorflow Week01
- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
- Convolutional Neural Networks in TensorFlow
- Natural Language Processing in TensorFlow
- Sequences, Time Series and Prediction
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
原來我之前已經完成 week01! XDD
那就速度把重要筆記 記錄下來 :)
Get started with Google Colaboratory (Coding TensorFlow)
- 各種 資料, model, service, ml container, tf module, vm image, trained model, techical guide ~~
In this exercise you’ll try to build a neural network that predicts the price of a house according to a simple formula.
So, imagine if house pricing was as easy as a house costs 50k + 50k per bedroom, so that a 1 bedroom house costs 100k, a 2 bedroom house costs 150k etc.
How would you create a neural network that learns this relationship so that it would predict a 7 bedroom house as costing close to 400k etc.
Hint: Your network might work better if you scale the house price down. You don’t have to give the answer 400…it might be better to create something that > predicts the number 4, and then your answer is in the ‘hundreds of thousands’ etc.
import tensorflow as tf import numpy as np from tensorflow import keras # GRADED FUNCTION: house_model def house_model(y_new): xs = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, ], dtype=float) ys = np.array([1, 1.5, 2., 2.5, 3.0, 3.5, 4.0, 4.5, ], dtype=float) model = keras.Sequential([keras.layers.Dense(units=1, input_shape=)]) model.compile(optimizer='sgd', loss='mean_squared_error') model.fit(xs, ys, epochs=500) return model.predict(y_new)
prediction = house_model([7.0]) print(prediction)
WARNING: Logging before flag parsing goes to stderr. W1128 07:32:25.905068 140150206101312 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor Epoch 1/500 8/8 [==============================] - 2s 274ms/sample - loss: 57.6285 Epoch 2/500 8/8 [==============================] - 0s 201us/sample - loss: 12.9565 Epoch 3/500 8/8 [==============================] - 0s 175us/sample - loss: 2.9200 Epoch 4/500 8/8 [==============================] - 0s 172us/sample - loss: 0.6651 ... ... ... Epoch 497/500 8/8 [==============================] - 0s 160us/sample - loss: 2.2577e-04 Epoch 498/500 8/8 [==============================] - 0s 163us/sample - loss: 2.2397e-04 Epoch 499/500 8/8 [==============================] - 0s 10ms/sample - loss: 2.2218e-04 Epoch 500/500 8/8 [==============================] - 0s 206us/sample - loss: 2.2041e-04 [4.0079846]
寫到這邊整個 很開心 很感動! 加油! 把 TensorFlow Developer Certificate 討取吧 ! :)