import numpy as np import pylab as pl from sklearn.datasets.samples_generator import make_moons %matplotlib inline # Functions for plotting 2D data and decision regions def plot_data(X, y): y_unique = np.unique(y) colors = pl.cm.rainbow(np.linspace(0.0, 1.0, y_unique.size)) for this_y, color in zip(y_unique, colors): this_X = X[y == this_y] pl.scatter(this_X[:, 0], this_X[:, 1], c=color, alpha=0.5, edgecolor='k', label="Class %s" % this_y) pl.legend(loc="best") pl.title("Data") def plot_decision_region(X, pred_fun): min_x = np.min(X[:, 0]) max_x = np.max(X[:, 0]) min_y = np.min(X[:, 1]) max_y = np.max(X[:, 1]) min_x = min_x - (max_x - min_x) * 0.05 max_x = max_x + (max_x - min_x) * 0.05 min_y = min_y - (max_y - min_y) * 0.05 max_y = max_y + (max_y - min_y) * 0.05 x_vals = np.linspace(min_x, max_x, 30) y_vals = np.linspace(min_y, max_y, 30) XX, YY = np.meshgrid(x_vals, y_vals) grid_r, grid_c = XX.shape ZZ = np.zeros((grid_r, grid_c)) for i in range(grid_r): for j in range(grid_c): ZZ[i, j] = pred_fun(XX[i, j], YY[i, j]) pl.contourf(XX, YY, ZZ, 30, cmap = pl.cm.coolwarm, vmin= 0, vmax=1) pl.colorbar() pl.xlabel("x") pl.ylabel("y")
X, Y = make_moons(n_samples=1000, noise= 0.2, random_state=3) x_train = X[:500] x_test = X[500:] y_train = Y[:500] y_test = Y[500:] pl.figure(figsize=(8, 6)) plot_data(x_train, y_train)
For this you will need to create a neural network with one hidden layer. You cannot use prebuilt models
such as those in
tf.estimator. Hint: extend the logistic regression example from the TensorFlow handout.
Your answer must contain the following:
Your goal is to improve the performance of the text classifier in the Keras handout. This is are the things that you need to try:
Try different combinations and report your findings at the end. Which configuration got the best accuracy in test?