128 lines
2.4 KiB
Python
128 lines
2.4 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Jan 23 09:59:50 2020
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@author: tanu
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"""
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#%%
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# LIDO ML: tensorflow
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#%%
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from sklearn.datasets import load_boston
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import MinMaxScaler
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import jrpytensorflow
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import tensorflow as tf
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import matplotlib.pyplot as plt
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boston = load_boston()
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#X, y = boston.data, boston.target
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_digits
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digits = load_digits()
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size = 0.2
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)
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#%%
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# P1
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X,y = jrpytensorflow.datasets.load_circles()
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plt.figure()
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plt.scatter(X[:,0], X[:,1], c = y, edgecolor = 'black')
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#2)
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size = 0.2
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)
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preprocess = Pipeline(
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steps = [
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('rescale', MinMaxScaler())
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]
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)
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preprocess.fit(X_train)
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X_train = preprocess.transform(X_train)
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X_test = preprocess.transform(X_test)
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import tensorflow as tf
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logModel = tf.keras.models.Sequential([
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tf.keras.layers.Dense(1, activation = 'sigmoid')
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])
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logModel.compile(optimizer = 'sgd',
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loss = 'binary_crossentropy')
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history = logModel.fit(X, y, epochs = 100)
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logModel.summary()
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#4) predicted probability: for X
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model(X) # tf object
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model.predict(X) # array
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model(X).numpy().ravel() > 0.5
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sum(model(X).numpy().ravel() > 0.5)
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#101
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sum( ( model(X).numpy().ravel() > 0.5) == y)
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#97
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# 4*) predicted probability: for X_test
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logModel.compile(optimizer = 'sgd',
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loss = 'binary_crossentropy')
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history = logModel.fit(X_test, y_test, epochs = 100)
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logModel.summary()
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model(X_test) # tf object
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model.predict(X_test) # array
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model(X_test).numpy().ravel() > 0.5
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sum( model(X_test).numpy().ravel() > 0.5)
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#22
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sum((model(X_test).numpy().ravel() > 0.5) == y_test)
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#21
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#%%
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# Practical 2
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#%%
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from tensorflow import keras
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def smallModel():
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model = tf.keras.models.Sequential([
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tf.keras.layers.Dense(20, activation = 'relu'),
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tf.keras.layers.Dense(10, activation = 'relu'),
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tf.keras.layers.Dense(1, activation = 'softmax')
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])
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model.compile(optimizer = 'sgd',
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loss = 'binary_crossentropy',
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metrics = ['accuracy'])
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return model
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model = smallModel()
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model.summary()
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