阿葛廷

人一定要靠自己!

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整個課程:

  1. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
  2. Convolutional Neural Networks in TensorFlow
  3. Natural Language Processing in TensorFlow
  4. 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)


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Exercise_1_House_Prices_Question

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=[1])])
    model.compile(optimizer='sgd', loss='mean_squared_error')
    model.fit(xs, ys, epochs=500)
    return model.predict(y_new)[0]

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]

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