tensorflow转化成matlab,将tensorflow导出到matlab
首先，我想我只需在屏幕上打印变量并将其复制到excel文件中，然后将其导出为.csv文件，以便在MATLAB中使用它们。在
但因为有太多的重量，这不是一个可行的选择。所以我用numpy将它们保存为.csv文件。一切都很好，但当我在Matlab中运行这个模型时，它似乎不起作用。当然有可能是我在Matlab代码中出错了。在
但是我注意到我的代码所打印的值与写入.csv文件的值不一样。在
由于我不熟悉tensorflow和python，所以我通过“缝合”不同的示例来编写代码。在
以下是tensorflow代码：#BREZ KONVOLUCIJE
import os
import os.path
import shutil
import tensorflow as tf
import numpy as np
LOGDIR = "/home/ubuntu/ml/tf-dev-summit-tensorboard-tutorial-master/mnist_NOCONV/"
LABELS = os.path.join(os.getcwd(), "labels_1024.tsv")
SPRITES = os.path.join(os.getcwd(), "sprite_1024.png")
### MNIST EMBEDDINGS ###
mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + "data", one_hot=True)
### Get a sprite and labels file for the embedding projector ###
if not (os.path.isfile(LABELS) and os.path.isfile(SPRITES)):
print("Necessary data files were not found. Run this command from inside the "
"repo provided at "
"https://github.com/dandelionmane/tf-dev-summit-tensorboard-tutorial.")
exit(1)
def fc_layer(input, size_in, size_out, name="fc"):
with tf.name_scope(name):
w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
act = tf.matmul(input, w) + b
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return act
def mnist_model(learning_rate, use_two_fc, use_two_conv, hparam):
tf.reset_default_graph()
sess = tf.Session()
# Setup placeholders, and reshape the data
x = tf.placeholder(tf.float32, shape=[None, 784], name="x")
x_image = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image('input', x_image, 10)
y = tf.placeholder(tf.float32, shape=[None, 10], name="labels")
# FC layer 1
w1 = tf.Variable(tf.truncated_normal([784, 1024], stddev=0.1), name="W1")
b1 = tf.Variable(tf.constant(0.1, shape=[1024]), name="B1")
act1 = tf.matmul(x, w1) + b1
tf.summary.histogram("weights", w1)
tf.summary.histogram("biases", b1)
tf.summary.histogram("activations", act1)
relu = tf.nn.relu(act1)
embedding_input = relu
embedding_size = 1024
tf.summary.histogram("fc1/relu", relu)
# FC layer 2
w2 = tf.Variable(tf.truncated_normal([1024, 10], stddev=0.1), name="W2")
b2 = tf.Variable(tf.constant(0.1, shape=[10]), name="B2")
logits = tf.matmul(relu, w2) + b2
tf.summary.histogram("weights", w2)
tf.summary.histogram("biases", b2)
tf.summary.histogram("activations", logits)
#if use_two_fc:
# fc1 = fc_layer(x, 784, 1024, "fc1")
# relu = tf.nn.relu(fc1)
# embedding_input = relu
#tf.summary.histogram("fc1/relu", relu)
# embedding_size = 1024
#logits = fc_layer(relu, 1024, 10, "fc2")
with tf.name_scope("xent"):
xent = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=y), name="xent")
tf.summary.scalar("xent", xent)
with tf.name_scope("train"):
train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar("accuracy", accuracy)
summ = tf.summary.merge_all()
embedding = tf.Variable(tf.zeros([1024, embedding_size]), name="test_embedding")
assignment = embedding.assign(embedding_input)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter(LOGDIR + hparam)
writer.add_graph(sess.graph)
config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
embedding_config = config.embeddings.add()
embedding_config.tensor_name = embedding.name
embedding_config.sprite.image_path = SPRITES
embedding_config.metadata_path = LABELS
# Specify the width and height of a single thumbnail.
embedding_config.sprite.single_image_dim.extend([28, 28])
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config)
for i in range(2001):
batch = mnist.train.next_batch(100)
if i % 5 == 0:
[train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: batch[0], y: batch[1]})
writer.add_summary(s, i)
if i % 500 == 0:
sess.run(assignment, feed_dict={x: mnist.test.images[:1024], y: mnist.test.labels[:1024]})
saver.save(sess, os.path.join(LOGDIR, "model.ckpt"), i)
sess.run(train_step, feed_dict={x: batch[0], y: batch[1]})
# Get the values of variables
w1_val, b1_val, w2_val, b2_val = sess.run([w1, b1, w2, b2])
np.savetxt("w1.csv", w1_val, delimiter=",")
np.savetxt("b1.csv", b1_val, delimiter=",")
np.savetxt("w2.csv", w2_val, delimiter=",")
np.savetxt("b2.csv", b2_val, delimiter=",")
tvars = tf.trainable_variables()
tvars_vals = sess.run(tvars)
print("\n \n \n")
for var, val in zip(tvars, tvars_vals):
print(var.name, val) # Prints the name of the variable alongside its value.
print("\n \n \n")
def main():
# You can try adding some more learning rates
learning_rate = 1E-4
use_two_fc = True
hparam = "OCR_2FC_NOCONV"
print('Starting run for %s' % hparam)
# Actually run with the new settings
mnist_model(learning_rate, use_two_fc, False, hparam)
print('Done training!')
print('Run `tensorboard --logdir=%s --host localhost --port 8088` to see the results.' % LOGDIR)
if __name__ == '__main__':
main()
以及MATLAB代码：
^{pr2}$
matlab中的softmax函数：
^{3}$
以及ReLU函数：% ReLU activivation function. We will need this later.
function f = ReLU(X)
f = arrayfun(@(x) ReLU0D(x),X);
end
%ReLU activation function for a scalar
function f = ReLU0D(x)
if x < 0
f = 0;
else
f = x;
end
end
结果是，Matlab代码的行为与它应该的一样(即res和为1)，但它只是在大多数时候得到错误的结果。但准确度应该在95%左右(用tensorflow测量)。我做错什么了？在
编辑：在Matlab中可视化图片效果很好。在
编辑：我正在添加我写的代码来制作.csv图片import gzip
import numpy as np
f = gzip.open('train-images-idx3-ubyte.gz','r')
image_size = 28
num_images = 20
f.read(16)
buf = f.read(image_size * image_size * num_images)
data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)
data = data.reshape(num_images, image_size, image_size, 1)
for i in range(num_images):
string = 'img'
string +=str(i)
string +=".csv"
image = np.asarray(data[i]).squeeze()
image= np.int32(image)
np.savetxt(string, image, delimiter=",")