Oct 19, 2020 日誌

看 neural_network_in_python.pdf

安裝 NVIDIA CUDA 10.1 和 cudnn 7.6.5

Download NVIDIA CUDA 10.1

https://developer.nvidia.com/cuda-10.1-download-archive-base

Download cudnn 7.6.5

https://developer.nvidia.com/rdp/cudnn-archive

Put the cudnn 7.6.5 in C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1

Run the tensorflow module.

TensorFlowKeras

import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.layers import Activation, Dense
import numpy as np
# X = input of our 3 input XOR gate
# set up the inputs of the neural network (right from the table)
X = np.array(([0,0,0],[0,0,1],[0,1,0], [0,1,1],[1,0,0],[1,0,1],[1,1,0],[1,1,1]), dtype=float)
# y = our output of our neural network
y = np.array(([1], [0], [0], [0], [0], [0], [0], [1]), dtype=float)
model = tf.keras.Sequential()
model.add(Dense(4, input_dim=3, activation='relu', use_bias=True))
#model.add(Dense(4, activation='relu', use_bias=True))
model.add(Dense(1, activation='sigmoid', use_bias=True))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['binary_accuracy'])
print (model.get_weights())
history = model.fit(X, y, epochs=2000, validation_data = (X, y))
model.summary()
# printing out to file
loss_history = history.history["loss"]
numpy_loss_history = np.array(loss_history)
np.savetxt("loss_history.txt", numpy_loss_history, delimiter="\n")
binary_accuracy_history = history.history["binary_accuracy"]
numpy_binary_accuracy = np.array(binary_accuracy_history)
np.savetxt("binary_accuracy.txt", numpy_binary_accuracy, delimiter="\n")
print(np.mean(history.history["binary_accuracy"]))
result = model.predict(X ).round()
print (result)