Google colab - keras-learn and Logistic Regression example

(1) Following the previous post, this demo the keras sample from how-to-install-tensorflow-with-gpu.html
Please take note that google only allow one active session for the free service. If you need faster GPU, more RAM and sessions, please consider to subscribe colab pro.

keraslearn.ipynb   Select all

# Step 1 mount google drive if data is from google drive import os from google.colab import drive drive.mount('/content/drive') # Step 2 if using tensorflow GPU #%tensorflow_version 2.x #import tensorflow as tf #print('TensorFlow: {}'.format(tf.__version__)) #tf.test.gpu_device_name() # Step 3 from keras.models import Sequential from keras.layers import Dense import numpy import time # fix random seed for reproducibility numpy.random.seed(7) # Step 4 # download pima indians dataset to google drive !curl -L https://tinyurl.com/tensorflowwin | grep -A768 pima-indians-diabetes.data.nbsp | sed '1d' > 'drive/MyDrive/Colab Notebooks/pima-indians-diabetes.data' # or download to local data directory !mkdir -p ./data !curl -L https://tinyurl.com/tensorflowwin | grep -A768 pima-indians-diabetes.data.nbsp | sed '1d' > './data/pima-indians-diabetes.data' # Step 5 load dataset from google drive dataset = numpy.loadtxt("drive/MyDrive/Colab Notebooks/pima-indians-diabetes.data", delimiter=",") # or load data from local data directory dataset = numpy.loadtxt("./data/pima-indians-diabetes.data", delimiter=",") # Step 6 # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # Step 7 # create model model = Sequential() model.add(Dense(12, input_dim=8, activation='relu')) model.add(Dense(1, activation='sigmoid')) # Step 8 # Compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # Step 9 start_time=time.time() # Fit the model model.fit(X, Y, batch_size=10, epochs=1500) # parameters for keras 1.2.2 # evaluate the model scores = model.evaluate(X, Y) print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100)) print("\nTraining took %.2f seconds\n" %(time.time()-start_time))

(2) For large training data set, consider to zip them and upload to google drive. Mount the google drive, then unzip it in local session. e.g.

!mkdir -p ./data
!unzip -o './drive/MyDrive/Colab Notebooks/mydata.zip' -d ./data/

(3) To stop the running cell in Google Colab use Ctrl-M I

(4) How to quickly run an ipynb example from github ?
4.1) Go to https://colab.research.google.com/, after login gmail and choose GitHub tab and enter search say "clareyan/From-Linear-to-Logistic-Regression-Explained-Step-by-Step"
4.2) In Step 2 cell box change the importing of dataset to
df = pd.read_csv('https://raw.githubusercontent.com/clareyan/From-Linear-to-Logistic-Regression-Explained-Step-by-Step/master/Social_Network_Ads.csv')
4.3) Then choose menu -> Runtime -> Run All. After that, use menu -> File -> Save a copy in Drive.