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Tensorflow feed_dictにリストを供給できません。コード:Tensorflow feed_dict浮動小数点のリストとInvalidArgumentError
InvalidArgumentError(トレースバックするための上記参照):
import tensorflow as tf import numpy as np from sklearn.model_selection import train_test_split from math import ceil BATCH_SIZE = 100 # Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3 x_data = np.random.rand(13000).astype(np.float32) y_data = x_data * 0.1 + 0.3 x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.3) # Try to find values for W and b that compute y_data = W * x_data + b # (We know that W should be 0.1 and b 0.3, but TensorFlow will # figure that out for us.) x_in = tf.placeholder(tf.float32, shape=[BATCH_SIZE], name='x_in') y_in = tf.placeholder(tf.float32, shape=[BATCH_SIZE], name='y_in') W = tf.Variable(tf.random_uniform([1], -1.0, 1.0)) b = tf.Variable(tf.zeros([1])) y = W * x_in + b # Minimize the mean squared errors. loss = tf.reduce_mean(tf.square(y - y_in)) optimizer = tf.train.GradientDescentOptimizer(0.5) train = optimizer.minimize(loss) # Before starting, initialize the variables. We will 'run' this first. init = tf.global_variables_initializer() # Launch the graph. sess = tf.Session() sess.run(init) batchesCount = ceil(len(x_train)/BATCH_SIZE) # Fit the line. for curBatchId in range(batchesCount): batchStart = curBatchId * BATCH_SIZE xf = x_train[batchStart: BATCH_SIZE] yf = y_train[batchStart: BATCH_SIZE] sess.run(train, feed_dict={x_in: xf, y_in:yf})
は私を与えるあなたはDTYPEフロートとプレースホルダテンソル 'x_in' の値 を供給し、[100]
を形成しなければなりません[ノード:x_in = Placeholderdtype = DT_FLOAT、形状= [100]、 _device = "/仕事:localhostの/レプリカ:0 /タスク:0/CPU:0"]]のコードで間違った
は何ですか、私の外見のためにそれはDTYPEフロートと
プレースホルダテンソル 'x_in' を通過するような形状と[100]
?