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CNNを構築しようとしていますが、input_samplesには8つのクラスがあり、各クラスには45のサンプルがあります。入力サンプルの総数は360です。私は最初の20サンプルを列車サンプルとして、残りの25サンプルを各クラスのテストサンプルとして分割しました（私の入力はテキストファイルであり、データは行内にあるので前処理済みデータです。テキストファイルの行を読み込み、16x12サイズの画像を再形成する）。デバッグ 'TypeError：CNNのndarrayをTensorまたはOperationに変換できません'
が、私はコード内のエラーを修正することができません
は私のコード：ファイルがdataset_300.txtとClassInfo.txtをダウンロードすることができます
import numpy as np
import random
import tensorflow as tf
folder = 'D:\\Lab_Project_Files\\TF\\Practice Files\\'
Datainfo = 'dataset_300.txt'
ClassInfo = 'classTrain.txt'
INPUT_WIDTH = 16
IMAGE_HEIGHT = 12
IMAGE_DEPTH = 1
IMAGE_PIXELS = INPUT_WIDTH * IMAGE_HEIGHT # 192 = 12*16
NUM_CLASSES = 8
STEPS = 500
STEP_VALIDATE = 100
BATCH_SIZE = 5
def load_data(file1,file2,folder):
filename1 = folder + file1
filename2 = folder + file2
# loading the data file
x_data = np.loadtxt(filename1, unpack=True)
x_data = np.transpose(x_data)
# loading the class information of the data loaded
y_data = np.loadtxt(filename2, unpack=True)
y_data = np.transpose(y_data)
# divide the data in to test and train data
x_data_train = x_data[np.r_[0:20, 45:65, 90:110, 135:155, 180:200, 225:245, 270:290, 315:335],:]
x_data_test = x_data[np.r_[20:45, 65:90, 110:135, 155:180, 200:225, 245:270, 290:315, 335:360], :]
y_data_train = y_data[np.r_[0:20, 45:65, 90:110, 135:155, 180:200, 225:245, 270:290, 315:335]]
y_data_test = y_data[np.r_[20:45, 65:90, 110:135, 155:180, 200:225, 245:270, 290:315, 335:360],:]
return x_data_train,x_data_test,y_data_train,y_data_test
def reshapedata(data_train,data_test):
data_train = np.reshape(data_train, (len(data_train),INPUT_WIDTH,IMAGE_HEIGHT))
data_test = np.reshape(data_test, (len(data_test), INPUT_WIDTH, IMAGE_HEIGHT))
return data_train,data_test
def batchdata(data,label, batchsize):
# generate random number required to batch data
order_num = random.sample(range(1, len(data)), batchsize)
data_batch = []
label_batch = []
for i in range(len(order_num)):
data_batch.append(data[order_num[i-1]])
label_batch.append(label[order_num[i-1]])
return data_batch, label_batch
# CNN trail
def conv_net(x):
weights = tf.Variable(tf.random_normal([INPUT_WIDTH * IMAGE_HEIGHT * IMAGE_DEPTH, NUM_CLASSES]))
biases = tf.Variable(tf.random_normal([NUM_CLASSES]))
out = tf.add(tf.matmul(x, weights), biases)
return out
sess = tf.Session()
# get filelist and labels for training and testing
data_train,data_test,label_train,label_test = load_data(Datainfo,ClassInfo,folder)
data_train, data_test, = reshapedata(data_train, data_test)
############################ get files for training ####################################################
image_batch, label_batch = batchdata(data_train,label_train,BATCH_SIZE)
# input output placeholders
x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS])
y_ = tf.placeholder(tf.float32,[None, NUM_CLASSES])
# create the network
y = conv_net(x)
# loss
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
# train step
train_step = tf.train.AdamOptimizer(1e-3).minimize(cost)
############################## get files for validataion ###################################################
image_batch_test, label_batch_test = batchdata(data_test,label_test,BATCH_SIZE)
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.initialize_all_variables())
################ CNN Program ##############################
for i in range(STEPS):
# checking the accuracy in between.
if i % STEP_VALIDATE == 0:
imgs, lbls = sess.run([image_batch_test, label_batch_test])
print(sess.run(accuracy, feed_dict={x: imgs, y_: lbls}))
imgs, lbls = sess.run([image_batch, label_batch])
sess.run(train_step, feed_dict={x: imgs, y_: lbls})
imgs, lbls = sess.run([image_batch_test, label_batch_test])
print(sess.run(accuracy, feed_dict={ x: imgs, y_: lbls}))
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