<p dir="auto"><em>Please make sure that this is a bug. As per our <a href="https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md">GitHub Policy</a>, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template</em></p>
<p dir="auto"><strong>System information</strong></p>
<ul dir="auto">
<li>Have I written custom code (as opposed to using a stock example script provided in TensorFlow): no</li>
<li>OS Platform and Distribution (e.g., Linux Ubuntu 16.04): colab</li>
<li>Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:</li>
<li>TensorFlow installed from (source or binary): tensorflow-gpu==2.0.0-beta1 colab</li>
<li>TensorFlow version (use command below):tensorflow-gpu==2.0.0-beta1</li>
<li>Python version: pip3</li>
<li>Bazel version (if compiling from source):</li>
<li>GCC/Compiler version (if compiling from source):</li>
<li>CUDA/cuDNN version: colab</li>
<li>GPU model and memory:</li>
</ul>
<p dir="auto"><strong>Describe the current behavior</strong><br>
if tf.keras.layers.Conv2D is used with dilation_rate param &gt; 1 it returns tensors with shape None!</p>
<p dir="auto"><em>Please make sure that this is a bug. As per our <a href="https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md">GitHub Policy</a>, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template</em></p>
<p dir="auto"><strong>System information</strong></p>
<ul dir="auto">
<li>Have I written custom code (as opposed to using a stock example script provided in TensorFlow):</li>
<li>OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04</li>
<li>Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: N/a</li>
<li>TensorFlow installed from (source or binary): binary</li>
<li>TensorFlow version (use command below): 2.0.0-beta0</li>
<li>Python version: 3.6</li>
<li>Bazel version (if compiling from source):</li>
<li>GCC/Compiler version (if compiling from source):</li>
<li>CUDA/cuDNN version: 10.0, 7.5</li>
<li>GPU model and memory: 11gb, GTX1080Ti</li>
</ul>
<p dir="auto">You can collect some of this information using our environment capture<br>
<a href="https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh">script</a><br>
You can also obtain the TensorFlow version with: 1. TF 1.0: <code class="notranslate">python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"</code> 2. TF 2.0: <code class="notranslate">python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"</code></p>
<p dir="auto"><strong>Describe the current behavior</strong><br>
I was using the code below with 1.12, 1.13.1 and tf2.0 alpha. But it fails to run in the latest tf2.0 beta. As you can see there is nothing fancy going on in the code. This is the block of code that produces this error</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="def ASPP(tensor):
    '''atrous spatial pyramid pooling'''
    dims = K.int_shape(tensor)

    y_pool = AveragePooling2D(pool_size=(
        dims[1], dims[2]), name='average_pooling')(tensor)
    y_pool = Conv2D(filters=256, kernel_size=1, padding='same',
                    kernel_initializer='he_normal', name='pool_1x1conv2d', use_bias=False)(y_pool)
    y_pool = BatchNormalization(name=f'bn_1')(y_pool)
    y_pool = Activation('relu', name=f'relu_1')(y_pool)

    y_pool = Upsample(tensor=y_pool, size=[dims[1], dims[2]])

    y_1 = Conv2D(filters=256, kernel_size=1, dilation_rate=1, padding='same',
                 kernel_initializer='he_normal', name='ASPP_conv2d_d1', use_bias=False)(tensor)
    y_1 = BatchNormalization(name=f'bn_2')(y_1)
    y_1 = Activation('relu', name=f'relu_2')(y_1)

    y_6 = Conv2D(filters=256, kernel_size=3, dilation_rate=6, padding='same',
                 kernel_initializer='he_normal', name='ASPP_conv2d_d6', use_bias=False)(tensor)
    y_6 = BatchNormalization(name=f'bn_3')(y_6)
    y_6 = Activation('relu', name=f'relu_3')(y_6)

    y_12 = Conv2D(filters=256, kernel_size=3, dilation_rate=12, padding='same',
                  kernel_initializer='he_normal', name='ASPP_conv2d_d12', use_bias=False)(tensor)
    y_12 = BatchNormalization(name=f'bn_4')(y_12)
    y_12 = Activation('relu', name=f'relu_4')(y_12)

    y_18 = Conv2D(filters=256, kernel_size=3, dilation_rate=18, padding='same',
                  kernel_initializer='he_normal', name='ASPP_conv2d_d18', use_bias=False)(tensor)
    y_18 = BatchNormalization(name=f'bn_5')(y_18)
    y_18 = Activation('relu', name=f'relu_5')(y_18)

    y = concatenate([y_pool, y_1, y_6, y_12, y_18], name='ASPP_concat')

    y = Conv2D(filters=256, kernel_size=1, dilation_rate=1, padding='same',
               kernel_initializer='he_normal', name='ASPP_conv2d_final', use_bias=False)(y)
    y = BatchNormalization(name=f'bn_final')(y)
    y = Activation('relu', name=f'relu_final')(y)
    return y"><pre class="notranslate"><code class="notranslate">def ASPP(tensor):
    '''atrous spatial pyramid pooling'''
    dims = K.int_shape(tensor)

    y_pool = AveragePooling2D(pool_size=(
        dims[1], dims[2]), name='average_pooling')(tensor)
    y_pool = Conv2D(filters=256, kernel_size=1, padding='same',
                    kernel_initializer='he_normal', name='pool_1x1conv2d', use_bias=False)(y_pool)
    y_pool = BatchNormalization(name=f'bn_1')(y_pool)
    y_pool = Activation('relu', name=f'relu_1')(y_pool)

    y_pool = Upsample(tensor=y_pool, size=[dims[1], dims[2]])

    y_1 = Conv2D(filters=256, kernel_size=1, dilation_rate=1, padding='same',
                 kernel_initializer='he_normal', name='ASPP_conv2d_d1', use_bias=False)(tensor)
    y_1 = BatchNormalization(name=f'bn_2')(y_1)
    y_1 = Activation('relu', name=f'relu_2')(y_1)

    y_6 = Conv2D(filters=256, kernel_size=3, dilation_rate=6, padding='same',
                 kernel_initializer='he_normal', name='ASPP_conv2d_d6', use_bias=False)(tensor)
    y_6 = BatchNormalization(name=f'bn_3')(y_6)
    y_6 = Activation('relu', name=f'relu_3')(y_6)

    y_12 = Conv2D(filters=256, kernel_size=3, dilation_rate=12, padding='same',
                  kernel_initializer='he_normal', name='ASPP_conv2d_d12', use_bias=False)(tensor)
    y_12 = BatchNormalization(name=f'bn_4')(y_12)
    y_12 = Activation('relu', name=f'relu_4')(y_12)

    y_18 = Conv2D(filters=256, kernel_size=3, dilation_rate=18, padding='same',
                  kernel_initializer='he_normal', name='ASPP_conv2d_d18', use_bias=False)(tensor)
    y_18 = BatchNormalization(name=f'bn_5')(y_18)
    y_18 = Activation('relu', name=f'relu_5')(y_18)

    y = concatenate([y_pool, y_1, y_6, y_12, y_18], name='ASPP_concat')

    y = Conv2D(filters=256, kernel_size=1, dilation_rate=1, padding='same',
               kernel_initializer='he_normal', name='ASPP_conv2d_final', use_bias=False)(y)
    y = BatchNormalization(name=f'bn_final')(y)
    y = Activation('relu', name=f'relu_final')(y)
    return y
</code></pre></div>
<p dir="auto">Strangely shapes of  y_pool, y_1 are correctly inferred , complete code to reproduce the issue available below</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="y = concatenate([y_pool, y_1, y_6, y_12, y_18], name='ASPP_concat')"><pre class="notranslate"><code class="notranslate">y = concatenate([y_pool, y_1, y_6, y_12, y_18], name='ASPP_concat')
</code></pre></div>
<p dir="auto"><strong>Describe the expected behavior</strong><br>
The code should work as it did in the earlier release ie tf2.0 alpha<br>
<strong>Code to reproduce the issue</strong></p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.models import Model
from tensorflow.keras.layers import AveragePooling2D, Lambda, Conv2D, Conv2DTranspose, Activation, Reshape, concatenate, Concatenate, BatchNormalization, ZeroPadding2D
from tensorflow.keras.applications.resnet50 import ResNet50


def Upsample(tensor, size):
    '''bilinear upsampling'''
    name = tensor.name.split('/')[0] + '_upsample'

    def bilinear_upsample(x, size):
        resized = tf.image.resize(
            images=x, size=size)
        return resized
    y = Lambda(lambda x: bilinear_upsample(x, size),
               output_shape=size, name=name)(tensor)
    return y


def ASPP(tensor):
    '''atrous spatial pyramid pooling'''
    dims = K.int_shape(tensor)

    y_pool = AveragePooling2D(pool_size=(
        dims[1], dims[2]), name='average_pooling')(tensor)
    y_pool = Conv2D(filters=256, kernel_size=1, padding='same',
                    kernel_initializer='he_normal', name='pool_1x1conv2d', use_bias=False)(y_pool)
    y_pool = BatchNormalization(name=f'bn_1')(y_pool)
    y_pool = Activation('relu', name=f'relu_1')(y_pool)

    y_pool = Upsample(tensor=y_pool, size=[dims[1], dims[2]])

    y_1 = Conv2D(filters=256, kernel_size=1, dilation_rate=1, padding='same',
                 kernel_initializer='he_normal', name='ASPP_conv2d_d1', use_bias=False)(tensor)
    y_1 = BatchNormalization(name=f'bn_2')(y_1)
    y_1 = Activation('relu', name=f'relu_2')(y_1)

    y_6 = Conv2D(filters=256, kernel_size=3, dilation_rate=6, padding='same',
                 kernel_initializer='he_normal', name='ASPP_conv2d_d6', use_bias=False)(tensor)
    y_6 = BatchNormalization(name=f'bn_3')(y_6)
    y_6 = Activation('relu', name=f'relu_3')(y_6)

    y_12 = Conv2D(filters=256, kernel_size=3, dilation_rate=12, padding='same',
                  kernel_initializer='he_normal', name='ASPP_conv2d_d12', use_bias=False)(tensor)
    y_12 = BatchNormalization(name=f'bn_4')(y_12)
    y_12 = Activation('relu', name=f'relu_4')(y_12)

    y_18 = Conv2D(filters=256, kernel_size=3, dilation_rate=18, padding='same',
                  kernel_initializer='he_normal', name='ASPP_conv2d_d18', use_bias=False)(tensor)
    y_18 = BatchNormalization(name=f'bn_5')(y_18)
    y_18 = Activation('relu', name=f'relu_5')(y_18)

    y = concatenate([y_pool, y_1, y_6, y_12, y_18], name='ASPP_concat')

    y = Conv2D(filters=256, kernel_size=1, dilation_rate=1, padding='same',
               kernel_initializer='he_normal', name='ASPP_conv2d_final', use_bias=False)(y)
    y = BatchNormalization(name=f'bn_final')(y)
    y = Activation('relu', name=f'relu_final')(y)
    return y


def DeepLabV3Plus(img_height, img_width):
    base_model = ResNet50(input_shape=(
        img_height, img_width, 3), weights='imagenet', include_top=False)

    image_features = base_model.get_layer('activation_39').output
    x_a = ASPP(image_features)
    x_a = Upsample(tensor=x_a, size=[img_height // 4, img_width // 4])

    x_b = base_model.get_layer('activation_9').output
    x_b = Conv2D(filters=48, kernel_size=1, padding='same',
                 kernel_initializer='he_normal', name='low_level_projection', use_bias=False)(x_b)
    x_b = BatchNormalization(name=f'bn_low_level_projection')(x_b)
    x_b = Activation('relu', name='low_level_activation')(x_b)

    x = concatenate([x_a, x_b], name='decoder_concat')

    x = Conv2D(filters=256, kernel_size=3, padding='same', activation='relu',
               kernel_initializer='he_normal', name='decoder_conv2d_1', use_bias=False)(x)
    x = BatchNormalization(name=f'bn_decoder_1')(x)
    x = Activation('relu', name='activation_decoder_1')(x)

    x = Conv2D(filters=256, kernel_size=3, padding='same', activation='relu',
               kernel_initializer='he_normal', name='decoder_conv2d_2', use_bias=False)(x)
    x = BatchNormalization(name=f'bn_decoder_2')(x)
    x = Activation('relu', name='activation_decoder_2')(x)
    x = Upsample(x, [img_height, img_width])

    x = Conv2D(1, (1, 1), name='output_layer')(x)
    x = Activation('sigmoid')(x)
    model = Model(inputs=base_model.input, outputs=x, name='DeepLabV3_Plus')
    return model"><pre class="notranslate"><code class="notranslate">import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.models import Model
from tensorflow.keras.layers import AveragePooling2D, Lambda, Conv2D, Conv2DTranspose, Activation, Reshape, concatenate, Concatenate, BatchNormalization, ZeroPadding2D
from tensorflow.keras.applications.resnet50 import ResNet50


def Upsample(tensor, size):
    '''bilinear upsampling'''
    name = tensor.name.split('/')[0] + '_upsample'

    def bilinear_upsample(x, size):
        resized = tf.image.resize(
            images=x, size=size)
        return resized
    y = Lambda(lambda x: bilinear_upsample(x, size),
               output_shape=size, name=name)(tensor)
    return y


def ASPP(tensor):
    '''atrous spatial pyramid pooling'''
    dims = K.int_shape(tensor)

    y_pool = AveragePooling2D(pool_size=(
        dims[1], dims[2]), name='average_pooling')(tensor)
    y_pool = Conv2D(filters=256, kernel_size=1, padding='same',
                    kernel_initializer='he_normal', name='pool_1x1conv2d', use_bias=False)(y_pool)
    y_pool = BatchNormalization(name=f'bn_1')(y_pool)
    y_pool = Activation('relu', name=f'relu_1')(y_pool)

    y_pool = Upsample(tensor=y_pool, size=[dims[1], dims[2]])

    y_1 = Conv2D(filters=256, kernel_size=1, dilation_rate=1, padding='same',
                 kernel_initializer='he_normal', name='ASPP_conv2d_d1', use_bias=False)(tensor)
    y_1 = BatchNormalization(name=f'bn_2')(y_1)
    y_1 = Activation('relu', name=f'relu_2')(y_1)

    y_6 = Conv2D(filters=256, kernel_size=3, dilation_rate=6, padding='same',
                 kernel_initializer='he_normal', name='ASPP_conv2d_d6', use_bias=False)(tensor)
    y_6 = BatchNormalization(name=f'bn_3')(y_6)
    y_6 = Activation('relu', name=f'relu_3')(y_6)

    y_12 = Conv2D(filters=256, kernel_size=3, dilation_rate=12, padding='same',
                  kernel_initializer='he_normal', name='ASPP_conv2d_d12', use_bias=False)(tensor)
    y_12 = BatchNormalization(name=f'bn_4')(y_12)
    y_12 = Activation('relu', name=f'relu_4')(y_12)

    y_18 = Conv2D(filters=256, kernel_size=3, dilation_rate=18, padding='same',
                  kernel_initializer='he_normal', name='ASPP_conv2d_d18', use_bias=False)(tensor)
    y_18 = BatchNormalization(name=f'bn_5')(y_18)
    y_18 = Activation('relu', name=f'relu_5')(y_18)

    y = concatenate([y_pool, y_1, y_6, y_12, y_18], name='ASPP_concat')

    y = Conv2D(filters=256, kernel_size=1, dilation_rate=1, padding='same',
               kernel_initializer='he_normal', name='ASPP_conv2d_final', use_bias=False)(y)
    y = BatchNormalization(name=f'bn_final')(y)
    y = Activation('relu', name=f'relu_final')(y)
    return y


def DeepLabV3Plus(img_height, img_width):
    base_model = ResNet50(input_shape=(
        img_height, img_width, 3), weights='imagenet', include_top=False)

    image_features = base_model.get_layer('activation_39').output
    x_a = ASPP(image_features)
    x_a = Upsample(tensor=x_a, size=[img_height // 4, img_width // 4])

    x_b = base_model.get_layer('activation_9').output
    x_b = Conv2D(filters=48, kernel_size=1, padding='same',
                 kernel_initializer='he_normal', name='low_level_projection', use_bias=False)(x_b)
    x_b = BatchNormalization(name=f'bn_low_level_projection')(x_b)
    x_b = Activation('relu', name='low_level_activation')(x_b)

    x = concatenate([x_a, x_b], name='decoder_concat')

    x = Conv2D(filters=256, kernel_size=3, padding='same', activation='relu',
               kernel_initializer='he_normal', name='decoder_conv2d_1', use_bias=False)(x)
    x = BatchNormalization(name=f'bn_decoder_1')(x)
    x = Activation('relu', name='activation_decoder_1')(x)

    x = Conv2D(filters=256, kernel_size=3, padding='same', activation='relu',
               kernel_initializer='he_normal', name='decoder_conv2d_2', use_bias=False)(x)
    x = BatchNormalization(name=f'bn_decoder_2')(x)
    x = Activation('relu', name='activation_decoder_2')(x)
    x = Upsample(x, [img_height, img_width])

    x = Conv2D(1, (1, 1), name='output_layer')(x)
    x = Activation('sigmoid')(x)
    model = Model(inputs=base_model.input, outputs=x, name='DeepLabV3_Plus')
    return model
</code></pre></div>
<p dir="auto"><strong>Other info / logs</strong></p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="ValueError                                Traceback (most recent call last)
&lt;ipython-input-20-0876af914f24&gt; in &lt;module&gt;()
----&gt; 1 DeepLabV3Plus(512, 512)

6 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/merge.py in build(self, input_shape)
    389                        'inputs with matching shapes '
    390                        'except for the concat axis. '
--&gt; 391                        'Got inputs shapes: %s' % (input_shape))
    392 
    393   def _merge_function(self, inputs):

ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 32, 32, 256), (None, 32, 32, 256), (None, None, None, 256), (None, None, None, 256), (None, None, None, 256)]"><pre class="notranslate"><code class="notranslate">ValueError                                Traceback (most recent call last)
&lt;ipython-input-20-0876af914f24&gt; in &lt;module&gt;()
----&gt; 1 DeepLabV3Plus(512, 512)

6 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/merge.py in build(self, input_shape)
    389                        'inputs with matching shapes '
    390                        'except for the concat axis. '
--&gt; 391                        'Got inputs shapes: %s' % (input_shape))
    392 
    393   def _merge_function(self, inputs):

ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 32, 32, 256), (None, 32, 32, 256), (None, None, None, 256), (None, None, None, 256), (None, None, None, 256)]
</code></pre></div>