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私はcnnとテンソルフローが新しいので、mnistデータセットでSVHNデータセットを実行するという例を適用しようとしました。画像は28x28の代わりに32x32で、1つではなく3つのカラーチャンネルがあります。テンソルフローのチュートリアルをSVHNデータに適用しようとするプレースホルダ問題
tensorflow.python.framework.errors.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float
私はまだ全体の頭や尾を作ることができません。たぶん誰かが私が確かに作ったいくつかの目障りなミスを抱えているかもしれません。私は残酷なことをお勧め:)ここ は、その全体が私のコードです：
import urllib
import os.path
import numpy as np
testfile = urllib.URLopener()
testfile2=urllib.URLopener()
import scipy.io as scp
if not os.path.isfile("test.mat"):
testfile.retrieve("http://ufldl.stanford.edu/housenumbers/test_32x32.mat", "test.mat")
if not os.path.isfile("train.mat"):
testfile.retrieve("http://ufldl.stanford.edu/housenumbers/train_32x32.mat", "train.mat")
testdata=scp.loadmat('test.mat')
traindata=scp.loadmat('train.mat')
trainDataX = traindata['X']
trainDataY = traindata['y']
testDataX = testdata['X']
testDataY = testdata['y']
def OnehotEndoding(Y):
Ytr=[]
for el in Y:
temp=np.zeros(10)
if el==10:
temp[0]=1
elif el==1:
temp[1]=1
elif el==2:
temp[2]=1
elif el==3:
temp[3]=1
elif el==4:
temp[4]=1
elif temp[5]==1:
temp[5]=1
elif temp[6]==1:
temp[6]=1
elif temp[7]==1:
temp[7]=1
elif temp[8]==1:
temp[8]=1
elif temp[9]==1:
temp[9]=1
Ytr.append(temp)
return np.asarray(Ytr)
trainDataY = OnehotEndoding(trainDataY)
testDataY = OnehotEndoding(testDataY)
def transposeArray(data):
print 'started'
xtrain = []
trainLen = data.shape[3]
print trainLen
for x in xrange(trainLen):
xtrain.append(data[:,:,:,x])
xtrain = np.asarray(xtrain)
return xtrain
trainDataX = transposeArray(trainDataX)
testDataX = transposeArray(testDataX)
print trainDataX.shape
import tensorflow as tf
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 32,32,3])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 3, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,32,32,3])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
x = tf.placeholder(tf.float32, shape=[None, 32,32,3])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
epoch=10000
batch_size=100
sess.run(tf.initialize_all_variables())
p = np.random.permutation(range(len(trainDataX)))
trX, trY = trainDataX[p], trainDataY[p]
print len(trainDataX)
start = 0
end = 0
for step in range(epoch):
start = end
end = start + batch_size
if start >= len(trainDataX):
start = 0
end = start + batch_size
if end >= len(trainDataX):
end = len(trainDataX) - 1
inX, outY = trX[start:end], trY[start:end]
#sess.run(optimizer, feed_dict= {tf_X: inX, tf_Y: outY, keep_prob:0.75})
if step % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={x: inX, y_: outY, keep_prob:1})
print("step %d, training accuracy %g"%(step, train_accuracy))
train_step.run(feed_dict={x: inX, y_: outY, keep_prob: 0.5})
print("test accuracy %g"%accuracy.eval(feed_dict={
x: testDataX, y_:testDataY , keep_prob: 1.0}))