模型权重不变Keras

Question

I am trying to create neural nets with random weights in keras. I am using set_weights() function of models, to assign random weights. However, model.predict() gives the same output on a certain input regardless of weights. The output differs every time I run the program, but it's same while a program is running. Here is the code:

ConnectFourAI.py:

from keras.models import Sequential
from keras.layers import Dense
from minimax2 import ConnectFour
import numpy as np
from time import sleep
import itertools
import random
import time

def get_model():

    model = Sequential()
    model.add(Dense(630, input_dim=84, kernel_initializer='uniform', activation='relu'))
    model.add(Dense(630,kernel_initializer='normal', activation='relu'))
    model.add(Dense(7, kernel_initializer='normal', activation='relu'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

map = {
    'x':[1,0],
    ' ':[0,0],
    'o':[0,1]
}

model = get_model()

def get_AI_move(grid):
    global model
    inp = np.array(list(itertools.chain.from_iterable([map[t] for t in np.array(grid).reshape(42)]))).reshape(1,84)
    nnout = model.predict(inp)
    # print(list(nnout[0]))
    out = np.argmax(nnout)
    while grid[0][out] != " ":
        out = np.random.randint(7)
    print("out = %d"%out)
    return out

shapes = [(w.shape) for w in model.get_weights()]

print(list(model.get_weights()[0][0][0:5]))
def score_func(x, win):
        if win == "x":
            return 10000
        elif win == " ":
            return 2000
        else:
            return x**2




if __name__=="__main__":

    for i in range(100):
        weights = [np.random.randn(*s) for s in shapes]
        # print(list(weights[0][0][0:5]))
        model.set_weights(weights)
        print(list(model.get_weights()[0][0][0:5]))
        game = ConnectFour()
        game.start_new()
        rounds = game._round
        win = game._winner
        score = score_func(rounds, win)
        print("%dth game scored %.3f"%(i+1,score))

        seed = int(time.time()* 10**6)%(2**32)+1
        np.random.seed(seed)

To recreate this error, you need an extra file. Everything is OK in this file, but the only call to random always gives the same value. Here is the file .

Answer 1

I don't know what exactly was going wrong, but I came up with a work around. Apparently there was some problem in random module due to which this behaviour took place when random module is called from 2 different files. So I used one file instead of two, and got the results I expected.