Reputation: 117
I am trying to create a CNN model with help of the following code:
import tensorflow as tf
class Create_CNN:
def _conv(self, input, nChannels, kernelSize, kernelStride):
conv = tf.layers.conv2d(inputs=input,
filters=nChannels,
kernel_size=kernelSize,
strides=(kernelStride, kernelStride),
padding='same',
activation=tf.nn.relu
)
return conv
def create_cnn(self, input, nChannels, kernelSize, kernelStride):
input = tf.reshape(input, shape=[-1, 500, 530, 3])
layer1 = Create_CNN()._conv(input, nChannels, kernelSize, kernelStride)
layer2 = Create_CNN()._conv(layer1, nChannels, kernelSize, kernelStride)
layer3 = Create_CNN()._conv(layer2, nChannels, kernelSize, kernelStride)
layer4 = Create_CNN()._conv(layer3, nChannels, kernelSize, kernelStride)
layer5 = Create_CNN()._conv(layer4, nChannels, kernelSize, kernelStride)
return layer5
Here, my input is a 500 * 530 * 3 dimension image. I am trying pass input and other parameters using following code:
with tf.Session().as_default():
tf.global_variables_initializer().run()
i = PlantUtils().create_instance('ara2013_plant001_rgb.png', 'ara2013_plant001_label.png', 500, 530, 100, 106, 1, 1,
1)
input_image = i[0] # It is a 500 * 530 * 3 tensor
b = Create_CNN().create_cnn(input=input_image, kernelSize=3, kernelStride=1, nChannels=30)
x = tf.argmax(input=b, axis=1)
print x.eval()
When I am trying to print logits(means value of x) I am getting the following error:
FailedPreconditionError (see above for traceback): Attempting to use uninitialized value conv2d_1/kernel
I am not sure what wrong I did. I need to see the logits generated from my CNN model. I really need help on this.
Upvotes: 1
Views: 4137
Reputation: 479
This is happening because you are running the initializer before building the graph. Ideally you should build the Graph
before creating a Session
. Try this
with tf.Graph().as_default():
i = PlantUtils().create_instance('ara2013_plant001_rgb.png', 'ara2013_plant001_label.png', 500, 530, 100, 106, 1, 1, 1)
input_image = i[0] # It is a 500 * 530 * 3 tensor
b = Create_CNN().create_cnn(input=input_image, kernelSize=3, kernelStride=1, nChannels=30)
x = tf.argmax(input=b, axis=1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print sess.run(x)
Upvotes: 3