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.

How to setup google colab and start linear regression with tensorflow.

(1) You only need a chrome browser, google gmail account and google drive account to start cloud tensorflow computing. And it is free to use and learn.

(2) Go to https://colab.research.google.com/
(3) Create a new notebook rename it and then Copy to Drive
(4) Type the following into notebook and run it step by step (Press Alt-Enter to run after each step)

LinearRegression.ipynb   Select all

#Step 1 # mount Google Drive, will ask for authorization code import numpy as np import os from google.colab import drive drive.mount('/content/drive') #Step 2 # choose the notebook settings to use GPU, via Menu -> Edit -> Notebook Settings. %tensorflow_version 2.x import tensorflow as tf ＃ will show GPU if successful tf.test.gpu_device_name() #Step 3 # load data import pandas as pd # either download the linear_data.csv and upload to google drive, or direct download it via the shell command as below !curl -L https://tinyurl.com/lineardatacsv | grep -A200 START_OF_LINEAR_DATA.CSV | sed '1d' | sed -n "/END_OF_LINEAR_DATA.CSV/q;p" | sed 's/&gt;/\>/g;s/&lt;/\</g' > 'drive/MyDrive/Colab Notebooks/linear_data.csv' df = pd.read_csv('drive/MyDrive/Colab Notebooks/linear_data.csv') df.head() #Step 4 # split into independent and dependent X = df[['X']].values y = df[['Y']].values X.shape, y.shape #Step 5 # visualize data import matplotlib.pyplot as plt %matplotlib inline plt.scatter(X,y) plt.xlabel('independent') plt.ylabel('dependent') plt.show() # Use Text box to enter ＃ # Linear Regression $ \hat y = a + b * X $ #Step 6 # Linear Regression # define regression model class regression(): def __init__(self): self.a = tf.Variable(initial_value=0,dtype=tf.float32) self.b = tf.Variable(initial_value=0,dtype=tf.float32) def __call__(self, X): x = tf.convert_to_tensor(X,dtype=tf.float32) y_est = tf.add(self.a, tf.multiply(self.b,x)) return y_est model = regression() # Use Text box to enter ＃ # loss = sum of square error (sse) = $ \sum (y_t - y_p) ^ 2 $ # step 7 # define loss function def loss_func(y_true, y_pred): # both values are in tensors sse = tf.reduce_sum(tf.square(tf.subtract(y_true,y_pred))) return sse # Use Text box to enter ＃ # Gradient Descent $ a = a_i - \nabla(sse) | a * LR $ $ b = b_i - \nabla(sse) | b * LR $ # step 8 # define train function def train(model, inputs, outputs, learning_rate): # convert outputs into tensor y_true = tf.convert_to_tensor(outputs,dtype=tf.float32) # GradientTape cal gradient distance with tf.GradientTape() as g: y_pred = model(inputs) current_loss = loss_func(y_true,y_pred) da,db = g.gradient(current_loss,[model.a,model.b]) # update the values model.a.assign_sub(da*learning_rate) model.b.assign_sub(db*learning_rate) # Step 9 def plot_scatter(x,y): plt.scatter(x,y) # scatter plt.plot(x,model(x),'r--') #line plot_scatter(X,y) # step 10 # model fitting model = regression() a_values = [] b_values = [] cost_values = [] # epochs, no of steps epochs = 100 # learning_rate learning_rate = 0.0001 for epoch in range(epochs): a_values.append(model.a) b_values.append(model.b) # prediction values and error y_pred = model(X) cost_value = loss_func(y,y_pred) cost_values.append(cost_value) # training train(model,X,y,learning_rate) # visual the scatter plot_scatter(X,y) plt.show #print the value print('Epoch: %d, Loss: %0.2f, a: %0.2f, b: %0.2f' %(epoch,cost_value,a_values[-1],b_values[-1])) # step 11 plt.plot(cost_values)

(5) Linear_data.csv , download and upload to google drive

linear_data.csv   Select all

X,Y 4,2 4,10 7,4 7,22 8,16 9,10 10,18 10,26 10,34 11,17 11,28 12,14 12,20 12,24 12,28 13,25 13,34 13,24 13,46 14,26 14,36 14,60 14,80 15,20 15,26 15,54 16,32 16,40 17,32 17,40 17,50 18,42 18,56 18,76 18,84 19,36 19,45 19,68 20,32 20,48 20,52