# Model class API In the functional API, given some input tensor(s) and output tensor(s), you can instantiate a `Model` via: “`python from keras.models import Model from keras.layers import Input, Dense a = Input(shape=(32,)) b = Dense(32)(a) model = Model(inputs=a, outputs=b) “` This model will include all layers required in the computation of `b` given […]
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## Usage of callbacks A callback is a set of functions to be applied at given stages of the training procedure. You can use callbacks to get a view on internal states and statistics of the model during training. You can pass a list of callbacks (as the keyword argument `callbacks`) to the `.fit()` method
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## Usage of metrics A metric is a function that is used to judge the performance of your model. Metric functions are to be supplied in the `metrics` parameter when a model is compiled. “`python model.compile(loss=’mean_squared_error’, optimizer=’sgd’, metrics=[‘mae’, ‘acc’]) “` “`python from keras import metrics model.compile(loss=’mean_squared_error’, optimizer=’sgd’, metrics=[metrics.mae, metrics.categorical_accuracy]) “` A metric function is similar
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# Agents An “agent” describes the method of running an RL algorithm against an environment in the gym. The agent may contain the algorithm itself or simply provide an integration between an algorithm and the gym environments. ## RandomAgent A sample agent located in this repo at `gym/examples/agents/random_agent.py`. This simple agent leverages the environments ability
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Regression – multiple features Second lecture on regression Srinandan Dasmahapatra Linear regression with multiple weights Arbitrary (linear/non-linear) but FIXED functions • • • • • • • • Recap simple fit of straight line through points, introduce intercept Flexibility of functions chosen to represent data Linear vs non-linear Fits with linear combinations of functions of
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TivaTM TM4C123GH6PM Microcontroller (identical to LM4F230H5QR) DATA SHEET DS-TM4C123GH6PM-15033.2672 Copyright © 2007-2013 SPMS376B Texas Instruments Incorporated TEXAS INSTRUMENTS-PRODUCTION DATA Copyright Copyright © 2007-2013 Texas Instruments Incorporated All rights reserved. Tiva and TivaWare are trademarks of Texas Instruments Incorporated. ARM and Thumb are registered trademarks and Cortex is a trademark of ARM Limited. Other names and
MNIST classification using LBP feature and SVM¶ Step 1: Import required packages¶ In [0]: import numpy as np import matplotlib.pyplot as plt from skimage import feature # This pacakge is used for LBP feature extraction from sklearn import svm # This pacakge is used for svm classification from sklearn import neighbors # this package is used
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COMP2420/COMP6420 – Introduction to Data Management, Analysis and Security Assignment – 2 Grading¶ Maximum Marks 100 Weight 20% of the Total Course Grade Submission deadline 7:00PM, Friday, May 24 Submission mode Electronic, Using GitLab One submission per group Estimated time 20 hours Penalty 100% after the deadline Submission¶ You need to submit the notebook Assignment-2.ipynb
Sentiment Classification: classifying IMDB reviews¶ In this task, you will learn how to process text data and how to train neural networks with limited input text data using pre-trained embeddings for sentiment classification (classifying a review document as “positive” or “negative” based solely on the text content of the review). We will use the Embedding
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