为了允许使用 Keras 模型作为标准 tensorflow 操作的一部分，我使用特定的输入占位符创建一个模型。
但是，当尝试执行 model.predict 时，我收到错误:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [100,84,84,4]
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[100,84,84,4], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
我的代码如下:
from keras.layers import Convolution2D, Dense, Input
from keras.models import Model
from keras.optimizers import Nadam
from keras.losses import mean_absolute_error
from keras.activations import relu
import tensorflow as tf
import numpy as np
import gym
state_size = [100, 84, 84, 4]
input_tensor = tf.placeholder(dtype=tf.float32, shape=state_size)
inputL = Input(tensor=input_tensor)
h1 = Convolution2D(filters=32, kernel_size=(5,5), strides=(4,4), activation=relu) (inputL)
h2 = Convolution2D(filters=64, kernel_size=(3,3), strides=(2,2), activation=relu) (h1)
h3 = Convolution2D(filters=64, kernel_size=(3,3), activation=relu) (h2)
h4 = Dense(512, activation=relu) (h3)
out = Dense(18) (h4)
model = Model(inputL, out)
opt = Nadam()
disc_rate=0.99
sess = tf.Session()
dummy_input = np.ones(shape=state_size)
model.compile(opt, mean_absolute_error)
writer = tf.summary.FileWriter('./my_graph', sess.graph)
writer.close()
print(out)
print(model.predict({input_tensor: dummy_input}))
我还尝试直接输入输入(没有字典，只有值) - 同样的异常(exception)。但是，我可以让模型像这样工作:
print(sess.run( model.output, {input_tensor: dummy_input }))
有没有办法让我仍然使用正常的 Keras .predict 方法？
最佳答案
以下工作(我们需要初始化全局变量):
sess.run(tf.global_variables_initializer()) # initialize
print(sess.run([model.output], feed_dict={input_tensor: dummy_input}))
关于machine-learning - 如何正确地将特定张量提供给 keras 模型，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/47421215/