Keras 固定随机数种子重现相同的训练结果

random_seed_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import *\n",
    "from keras.models import *\n",
    "from keras.datasets.mnist import load_data\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = load_data()\n",
    "X_train, X_test = X_train / 255.0, X_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/ypw/miniconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /Users/ypw/miniconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From /Users/ypw/miniconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "60000/60000 [==============================] - 2s 34us/step - loss: 1.1246 - acc: 0.7235 - val_loss: 0.6050 - val_acc: 0.8626\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11edfb320>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(2019)\n",
    "tf.set_random_seed(2019)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Flatten(input_shape=X_train.shape[1:]))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='sgd', \n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train, batch_size=128, epochs=1, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.08332505, -0.04046969, -0.10420379, ...,  0.01150452,\n",
       "         0.00657567,  0.09461053],\n",
       "       [-0.06537206, -0.00617846, -0.01010726, ..., -0.09795328,\n",
       "         0.03855512, -0.09573275],\n",
       "       [ 0.06116908, -0.02009934, -0.0755541 , ...,  0.10054862,\n",
       "         0.01570166,  0.1094622 ],\n",
       "       ...,\n",
       "       [ 0.02430227,  0.08767582, -0.14034536, ..., -0.0235549 ,\n",
       "        -0.06785435,  0.07870072],\n",
       "       [-0.1178989 ,  0.04752532,  0.1322875 , ..., -0.01668982,\n",
       "         0.03100156,  0.08343341],\n",
       "       [ 0.04044323, -0.08505493,  0.01293952, ..., -0.0128099 ,\n",
       "         0.0589669 ,  0.09185769]], dtype=float32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights1 = K.get_value(model.layers[-1].weights[0])\n",
    "weights1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/1\n",
      "60000/60000 [==============================] - 2s 35us/step - loss: 1.1246 - acc: 0.7235 - val_loss: 0.6050 - val_acc: 0.8626\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x140fe7f28>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(2019)\n",
    "tf.set_random_seed(2019)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Flatten(input_shape=X_train.shape[1:]))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='sgd', \n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train, batch_size=128, epochs=1, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.08332505, -0.04046969, -0.10420379, ...,  0.01150452,\n",
       "         0.00657567,  0.09461053],\n",
       "       [-0.06537206, -0.00617846, -0.01010726, ..., -0.09795328,\n",
       "         0.03855512, -0.09573275],\n",
       "       [ 0.06116908, -0.02009934, -0.0755541 , ...,  0.10054862,\n",
       "         0.01570166,  0.1094622 ],\n",
       "       ...,\n",
       "       [ 0.02430227,  0.08767582, -0.14034536, ..., -0.0235549 ,\n",
       "        -0.06785435,  0.07870072],\n",
       "       [-0.1178989 ,  0.04752532,  0.1322875 , ..., -0.01668982,\n",
       "         0.03100156,  0.08343341],\n",
       "       [ 0.04044323, -0.08505493,  0.01293952, ..., -0.0128099 ,\n",
       "         0.0589669 ,  0.09185769]], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights2 = K.get_value(model.layers[-1].weights[0])\n",
    "weights2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(weights1 == weights2).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

我在google colab无法重现 你tf和keras版本是？

Train on 60000 samples, validate on 10000 samples
Epoch 1/2
60000/60000 [==============================] - 4s 61us/step - loss: 0.2117 - acc: 0.9365 - val_loss: 0.1123 - val_acc: 0.9645
Epoch 2/2
60000/60000 [==============================] - 3s 54us/step - loss: 0.0834 - acc: 0.9755 - val_loss: 0.0853 - val_acc: 0.9724
<keras.callbacks.History at 0x7f37e8751dd8>

Train on 60000 samples, validate on 10000 samples
Epoch 1/2
60000/60000 [==============================] - 4s 65us/step - loss: 0.2067 - acc: 0.9375 - val_loss: 0.1110 - val_acc: 0.9654
Epoch 2/2
60000/60000 [==============================] - 3s 53us/step - loss: 0.0805 - acc: 0.9759 - val_loss: 0.0831 - val_acc: 0.9735
<keras.callbacks.History at 0x7f37e87f2b38>

需要使用CPU运行。