Keras模型爲make_moons數據創建線性分類

Question

我試圖在此WildML - Implementing a Neural Network From Scratch教程中重現模型，但使用Keras代替。我嘗試使用與教程相同的所有配置，但即使在調整了時代，批量大小，激活函數和隱藏層中的單元數後，我仍然得到了線性分類：

這裏是我的代碼：

from keras.models import Sequential 
from keras.layers import Dense, Activation 
from keras.utils.visualize_util import plot 
from keras.utils.np_utils import to_categorical 

import numpy as np 
import matplotlib.pyplot as plt 

import sklearn 
from sklearn import datasets, linear_model 

# Build model 
model = Sequential() 
model.add(Dense(input_dim=2, output_dim=3, activation="tanh", init="normal")) 
model.add(Dense(output_dim=2, activation="softmax")) 
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) 

# Train 
np.random.seed(0) 
X, y = sklearn.datasets.make_moons(200, noise=0.20) 
y_binary = to_categorical(y) 
model.fit(X, y_binary, nb_epoch=100) 

# Helper function to plot a decision boundary. 
# If you don't fully understand this function don't worry, it just generates the contour plot below. 
def plot_decision_boundary(pred_func): 
    # Set min and max values and give it some padding 
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5 
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5 
    h = 0.01 
    # Generate a grid of points with distance h between them 
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) 
    # Predict the function value for the whole gid 
    Z = pred_func(np.c_[xx.ravel(), yy.ravel()]) 
    Z = Z.reshape(xx.shape) 
    # Plot the contour and training examples 
    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) 
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral) 

# Predict and plot 
plot_decision_boundary(lambda x: model.predict_classes(x, batch_size=200)) 
plt.title("Decision Boundary for hidden layer size 3") 
plt.show() 

Answer 1

我相信我想通了這個問題。如果我刪除np.random.seed(0)，火車2000點時代，輸出不總是線性的，偶爾獲得更高的90％+精度：

這肯定是這樣np.random.seed(0)導致被接種不良的參數，因爲我正在修理隨機播種，所以每次都會看到相同的圖形。