Question

我想在过去的3天内让一个简单的CNN训练。

首先，我设置了一个输入管道/队列配置，从目录树中读取图像并准备批次。

我在此link获得了此代码。所以，我现在有 train_image_batch 和 train_label_batch ，我需要提供给我的CNN。

train_image_batch, train_label_batch = tf.train.batch(
        [train_image, train_label],
        batch_size=BATCH_SIZE
        # ,num_threads=1
    )

我无法弄清楚如何。我正在使用此link给出的CNN代码。

# Input Layer
input_layer = tf.reshape(train_image_batch, [-1, IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNELS])

# Convolutional Layer #1
conv1 = new_conv_layer(input_layer, NUM_CHANNELS, 5, 32, 2)

 # Pooling Layer #1
pool1 = new_pooling_layer(conv1, 2, 2)

打印时的input_layer显示此

Tensor（“Reshape：0”，shape =（5,120,120,3），dtype = uint8）

下一行与TypeError崩溃; conv1 = new_conv_layer（...）。 new_conv_layer函数的主体在下面给出

def new_conv_layer(input,              # The previous layer.
               num_input_channels, # Num. channels in prev. layer.
               filter_size,        # Width and height of each filter.
               num_filters,        # Number of filters.
               stride):

# Shape of the filter-weights for the convolution.
# This format is determined by the TensorFlow API.
shape = [filter_size, filter_size, num_input_channels, num_filters]

# Create new weights aka. filters with the given shape.
weights = tf.Variable(tf.truncated_normal(shape, stddev=0.05))

# Create new biases, one for each filter.
biases = tf.Variable(tf.constant(0.05, shape=[num_filters]))

# Create the TensorFlow operation for convolution.
# Note the strides are set to 1 in all dimensions.
# The first and last stride must always be 1,
# because the first is for the image-number and
# the last is for the input-channel.
# But e.g. strides=[1, 2, 2, 1] would mean that the filter
# is moved 2 pixels across the x- and y-axis of the image.
# The padding is set to 'SAME' which means the input image
# is padded with zeroes so the size of the output is the same.
layer = tf.nn.conv2d(input=input,
                     filter=weights,
                     strides=[1, stride, stride, 1],
                     padding='SAME')

# Add the biases to the results of the convolution.
# A bias-value is added to each filter-channel.
layer += biases

# Rectified Linear Unit (ReLU).
# It calculates max(x, 0) for each input pixel x.
# This adds some non-linearity to the formula and allows us
# to learn more complicated functions.
layer = tf.nn.relu(layer)

# Note that ReLU is normally executed before the pooling,
# but since relu(max_pool(x)) == max_pool(relu(x)) we can
# save 75% of the relu-operations by max-pooling first.

# We return both the resulting layer and the filter-weights
# because we will plot the weights later.
return layer, weights

此错误恰好在 tf.nn.conv2d 崩溃

TypeError：传递给参数'input'的值的DataType uint8不在允许值列表中：float16，float32

Answer 1

来自输入管道的图片类型为＆＃39; uint8＆＃39;，您需要输入“＃32; float32＆＃39;”，您可以在图片jpeg解码器之后执行此操作：

image = tf.image.decode_jpeg(...
image = tf.cast(image, tf.float32)

TensorFlow TypeError：传递给参数输入的值的DataType uint8不在允许值列表中：float16，float32

1 个答案: