pdfcoffee
Chapter 3One can also use TensorBoard to see how the weights and bias of the model weremodified as the network underwent training. In the following graph we can seethat with each time step the bias changed. We can see that as the model is learning(x-axis – time), the bias is spreading from an initial value of 0:SummaryThis chapter dealt with different types of regression algorithms. We started withlinear regression and used it to predict house prices for a simple one-input variablecase and for multiple input variable cases. The chapter then moved towards logisticregression, which is a very important and useful technique for classifying tasks.The chapter explained the TensorFlow Estimator API and used it to implementboth linear and logistic regression for some classical datasets. The next chapter willintroduce you to convolutional neural networks, the most commercially successfulneural network models.[ 107 ]
RegressionReferencesHere are some good resources if you are interested in knowing more about theconcepts we've covered in this chapter:• https://www.tensorflow.org/• https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data• https://onlinecourses.science.psu.edu/stat501/node/250[ 108 ]
- Page 92 and 93: • To create random values from a
- Page 94 and 95: To know the value, we need to creat
- Page 96 and 97: Chapter 2Both PyTorch and TensorFlo
- Page 98 and 99: Chapter 2state = [tf.zeros([100, 10
- Page 100 and 101: Chapter 2For now, there's no need t
- Page 102 and 103: Chapter 2Let's see an example of a
- Page 104 and 105: Chapter 2If you want to save a mode
- Page 106 and 107: Chapter 2supervised=True)train_data
- Page 108 and 109: Chapter 2There, tf.feature_column.n
- Page 110 and 111: Chapter 2print (dz_dx)print (dy_dx)
- Page 112 and 113: Chapter 2In our toy example we use
- Page 114 and 115: Chapter 2For multi-machine training
- Page 116 and 117: Chapter 25. Use tf.layers modules t
- Page 118 and 119: Chapter 2Keras or tf.keras?Another
- Page 120: • tf.data can be used to load mod
- Page 123 and 124: RegressionLet us imagine a simpler
- Page 125 and 126: RegressionTake a look at the last t
- Page 127 and 128: Regression3. Now, we calculate the
- Page 129 and 130: RegressionIn the next section we wi
- Page 131 and 132: Regression2. Now, we define the fea
- Page 133 and 134: Regression2. Download the dataset:(
- Page 135 and 136: RegressionThe following is the Tens
- Page 137 and 138: RegressionIn regression the aim is
- Page 139 and 140: RegressionThe Estimator outputs the
- Page 141: RegressionThe following is the grap
- Page 145 and 146: Convolutional Neural NetworksIn thi
- Page 147 and 148: Convolutional Neural NetworksIn thi
- Page 149 and 150: Convolutional Neural NetworksIn oth
- Page 151 and 152: Convolutional Neural NetworksThen w
- Page 153 and 154: Convolutional Neural NetworksHoweve
- Page 155 and 156: Convolutional Neural NetworksPlotti
- Page 157 and 158: Convolutional Neural NetworksIn gen
- Page 159 and 160: Convolutional Neural NetworksOur ne
- Page 161 and 162: Convolutional Neural NetworksThese
- Page 163 and 164: Convolutional Neural NetworksSo, we
- Page 165 and 166: Convolutional Neural NetworksEach i
- Page 167 and 168: Convolutional Neural NetworksVery d
- Page 169 and 170: Convolutional Neural NetworksRecogn
- Page 171 and 172: Convolutional Neural NetworksIf we
- Page 173 and 174: Convolutional Neural NetworksRefere
- Page 175 and 176: Advanced Convolutional Neural Netwo
- Page 177 and 178: Advanced Convolutional Neural Netwo
- Page 179 and 180: Advanced Convolutional Neural Netwo
- Page 181 and 182: Advanced Convolutional Neural Netwo
- Page 183 and 184: Advanced Convolutional Neural Netwo
- Page 185 and 186: Advanced Convolutional Neural Netwo
- Page 187 and 188: Advanced Convolutional Neural Netwo
- Page 189 and 190: Advanced Convolutional Neural Netwo
- Page 191 and 192: Advanced Convolutional Neural Netwo
Chapter 3
One can also use TensorBoard to see how the weights and bias of the model were
modified as the network underwent training. In the following graph we can see
that with each time step the bias changed. We can see that as the model is learning
(x-axis – time), the bias is spreading from an initial value of 0:
Summary
This chapter dealt with different types of regression algorithms. We started with
linear regression and used it to predict house prices for a simple one-input variable
case and for multiple input variable cases. The chapter then moved towards logistic
regression, which is a very important and useful technique for classifying tasks.
The chapter explained the TensorFlow Estimator API and used it to implement
both linear and logistic regression for some classical datasets. The next chapter will
introduce you to convolutional neural networks, the most commercially successful
neural network models.
[ 107 ]
Change language