hw1: wip
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8 changed files with 83 additions and 26 deletions
assignment_1
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assignment_1/deliverable/linear_regression.pickle
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assignment_1/deliverable/linear_regression.pickle
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assignment_1/deliverable/nonlinear_model/keras_metadata.pb
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assignment_1/deliverable/nonlinear_model/keras_metadata.pb
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assignment_1/deliverable/nonlinear_model/saved_model.pb
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assignment_1/deliverable/nonlinear_model/saved_model.pb
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assignment_1/deliverable/nonlinear_model_normalizers.pickle
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assignment_1/deliverable/nonlinear_model_normalizers.pickle
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assignment_1/deliverable/run_model.py
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assignment_1/deliverable/run_model.py
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@ -1,6 +1,10 @@
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#!/usr/bin/env python3
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import joblib
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import numpy as np
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from keras import models
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import sys
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from keras.models import Model
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def load_data(filename):
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"""
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@ -57,13 +61,51 @@ if __name__ == '__main__':
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# EDITABLE SECTION OF THE SCRIPT: if you need to edit the script, do it here
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############################################################################
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# Load the trained model
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baseline_model_path = './baseline_model.pickle'
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baseline_model = load_model(baseline_model_path)
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ms = {"baseline", "linear", "nonlinear"}
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# Predict on the given samples
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y_pred = baseline_model.predict(x)
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if len(sys.argv) != 2:
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print("%s {model_name}" % sys.argv[0])
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sys.exit(1)
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if sys.argv[1] not in ms:
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print("model name must be one of " + str(ms))
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sys.exit(2)
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y_pred = None
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if sys.argv[1] == "baseline":
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# Load the trained model
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baseline_model_path = './baseline_model.pickle'
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baseline_model = load_model(baseline_model_path)
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# Predict on the given samples
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y_pred = baseline_model.predict(x)
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else:
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X = np.zeros([x.shape[0], 5])
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X[:, 0] = 1
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X[:, 1:3] = x
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X[:, 3] = x[:, 0] * x[:, 1]
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X[:, 4] = np.sin(x[:, 0])
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if sys.argv[1] == "linear":
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# Load the linear model
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linear_model_path = './linear_regression.pickle'
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linear_model = load_model(linear_model_path)
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# Predict on the given samples
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y_pred = linear_model.predict(X)
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else:
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# Load saved Keras model
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nonlinear_model = models.load_model('./nonlinear_model')
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# Load saved normalizing factors for Xs
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normalizers = load_model('./nonlinear_model_normalizers.pickle')
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# Normalize Xs with normalizing factors
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X = X[:, 1:3]
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X -= normalizers["mean"]
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X /= normalizers["std"]
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# Run model on normalized data
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y_pred = nonlinear_model.predict(X)
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y_pred = y_pred[:, 0]
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############################################################################
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# STOP EDITABLE SECTION: do not modify anything below this point.
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@ -2,14 +2,18 @@ import numpy as np
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from sklearn.linear_model import LinearRegression
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from sklearn.model_selection import train_test_split
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from tensorflow.keras import Input, Model
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.models import Sequential, save_model
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from tensorflow.keras.layers import Dense
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from tensorflow.keras import losses
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from tensorflow import keras
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import tensorflow as tf
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from sklearn.metrics import mean_squared_error
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from utils import save_sklearn_model, save_keras_model
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import os
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data = np.load("../data/data.npz")
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d = os.path.dirname(__file__)
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data = np.load(d + "/../data/data.npz")
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xs = data["x"] # 2000x2
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y = data["y"] # 2000x1
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points = 2000
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@ -43,6 +47,8 @@ print("# Linear regression:")
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# Print the resulting parameters
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print("f(x) = %g + %g * x_1 + %g * x_2 + %g * x_1 * x_2 + %g * sin(x_1)" % tuple(reg.coef_))
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save_sklearn_model(reg, d + "/../deliverable/linear_regression.pickle")
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# Test using MSQ on test set
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score = reg.score(X_test, y_test)
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print("MSQ error on test set is: %g" % (score))
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@ -53,48 +59,45 @@ print("\n# Feed-forward NN:")
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A = X_val
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X_train = X_train[:, 1:]
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X_val = X_val[:, 1:]
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X_train = X_train[:, 1:3]
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X_val = X_val[:, 1:3]
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# X_train = X_train[:, 1:3]
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# X_val = X_val[:, 1:3]
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mean = np.mean(X_train, axis=0)
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std = np.std(X_train, axis=0)
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#y_mean = np.mean(y_train, axis=0)
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#y_std = np.std(y_train, axis=0)
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y_mean = 0
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y_std = 1
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X_train -= mean
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X_train /= std
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y_train -= y_mean
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y_train /= y_std
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X_val -= mean
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X_val /= std
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y_val -= y_mean
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y_val /= y_std
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network = Sequential()
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network.add(Dense(35, activation='relu'))
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network.add(Dense(30, activation='relu'))
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network.add(Dense(20, activation='relu'))
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network.add(Dense(20, activation='relu'))
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network.add(Dense(10, activation='relu'))
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network.add(Dense(5, activation='relu'))
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network.add(Dense(3, activation='relu'))
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network.add(Dense(2, activation='relu'))
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network.add(Dense(1, activation='linear'))
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network.compile(optimizer='rmsprop', loss='mse', metrics=['mse'])
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epochs = 10000
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callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=1000)
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network.fit(X_train, y_train, epochs=epochs, verbose=1, batch_size=100, validation_data=(X_val, y_val), callbacks=[callback])
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epochs = 100000
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callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=40)
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network.fit(X_train, y_train, epochs=epochs, verbose=1, batch_size=15,
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validation_data=(X_val, y_val), callbacks=[callback])
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network.save(d + "/../deliverable/nonlinear_model")
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save_sklearn_model({"mean": mean, "std": std}, d + "/../deliverable/nonlinear_model_normalizers.pickle")
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msq = mean_squared_error(network.predict(X_val), y_val)
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print(msq)
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X_test = X_test[:, 1:]
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X_test = X_test[:, 1:3]
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X_test -= mean
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X_test /= std
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y_test -= y_mean
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y_test /= y_std
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msq = mean_squared_error(network.predict(X_test), y_test)
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print(msq)
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