After Flatten() both batch and output shape of a tensor become none

If I use Flatten() using functional API, both batch and output shape become none, where as in Sequential model its print correct output shape. I need both batch and output size later on and I have to use functional api because of my model complexity, is this is a issue of Keras? @fchollet @farizrahman4u @Dref360

`input = Input(batch_shape=[64,224,224,3])

test = Conv2D(96,kernel_size=(11,11),strides=(4,4), activation=‘relu’,name=‘conv1’)(input) print 'b4 flatten shape ', test.get_shape()

test = Flatten()(test)

print 'after flateen shape ',test.get_shape()

alexnet = Sequential()

alexnet.add(InputLayer(batch_input_shape=img_input))

alexnet.add(Conv2D(96, kernel_size=(11, 11), strides=(4, 4),
                   activation='relu', name='conv1'))

alexnet.add(Flatten())

print 'Sequential model shape ',alexnet.output_shape`

The output I got :

b4 flatten shape (64, 30, 30, 96) after flatten shape (?, ?) Sequential model shape (64, 86400)

[thomasmooon]

My current workaround is to query the shape before Flatten() is done this way:

# imports
import numpy as np
import keras
from keras.layers import *

# toy net
x = Input(shape = (12,100,10))
x = Dense(32)(x)
f = Flatten()(x)

# shape inference
shape_before_flatten = x.shape.as_list()[1:] # [1:] to skip None
shape_flatten = np.prod(shape_before_flatten) # value of shape in the non-batch dimension

# print
print("x = "+ str(x))
print("shape before Flatten() = " + str(shape_before_flatten))
print("shape after Flatten() = " + str(f.shape.as_list()))
print("shape_flatten in the non-batch dimension: " + str(shape_flatten))

x = Tensor(“dense_17/BiasAdd:0”, shape=(?, 12, 100, 32), dtype=float32) shape before Flatten() = [12, 100, 32] shape after Flatten() = [None, None] shape_flatten in the non-batch dimension: 38400

[demba]

Hi, I am using Keras 2.1.5 and am getting similar issues with Flatten() and Reshape not behaving as expected. The output of the same commands as above are

Tensor("input_5:0", shape=(16, 10, 10), dtype=float32)
Tensor("flatten_15/Reshape:0", shape=(?, ?), dtype=float32)
Tensor("reshape_27/Reshape:0", shape=(?, ?), dtype=float32)

The work around suggested in the previous comment doesn’t work. My current workaround is

x = Input(batch_shape=(16, 10, 10))
x = keras.layers.Reshape((100,))(x)
print(x)

which outputs

Tensor("reshape_29/Reshape:0", shape=(16, 100), dtype=float32)