tensorflow - 如何解决 AssertionError: 无法计算输出 Tensor("conv2d_16/BiasAdd:0", shape=(None, 64, 64, 3), dtype=float32)？

Question

我正在尝试实现 GAN。我越来越AssertionError: Could not compute output Tensor("conv2d_16/BiasAdd:0", shape=(None, 64, 64, 3), dtype=float32)。我不知道原因。需要帮助。

这是代码示例。

S = keras.models.Sequential()

S.add(keras.layers.Dense(codings_size, input_shape = [codings_size]))
S.add(keras.layers.LeakyReLU(0.2))
S.add(keras.layers.Dense(codings_size))
S.add(keras.layers.LeakyReLU(0.2))
S.add(keras.layers.Dense(codings_size))
S.add(keras.layers.LeakyReLU(0.2))
S.add(keras.layers.Dense(codings_size))
S.add(keras.layers.LeakyReLU(0.2))

S.compile(loss = 'mse', optimizer = keras.optimizers.Adam(lr=0.0002))

# === Generator ===

#Inputs
inp_style = []

for i in range(5):
    inp_style.append(keras.layers.Input([codings_size]))

inp_noise = keras.layers.Input([64, 64, 1])

#Latent
x = keras.layers.Lambda(lambda x: x[:, :128])(inp_style[0])

#Actual Model
x = keras.layers.Dense(4*4*4*8, activation = 'relu', kernel_initializer = 'he_normal')(x)
x = keras.layers.Reshape([4, 4, 4*8])(x)
x = g_block(x, inp_style[0], inp_noise, 16 * 8, u = False)  #4
x = g_block(x, inp_style[1], inp_noise, 8 * 8)  #8
x = g_block(x, inp_style[2], inp_noise, 6 * 8)  #16
x = g_block(x, inp_style[3], inp_noise, 4 * 8)  #32
x = g_block(x, inp_style[4], inp_noise, 3 * 8)   #64

x = keras.layers.Conv2D(filters = 3, kernel_size = 1, padding = 'same', kernel_initializer = 'he_normal')(x)

generator = keras.models.Model(inputs = inp_style + [inp_noise], outputs = x)

inp_ = keras.layers.Input(shape=list(faces.image_shape))
d = keras.layers.Conv2D(32, kernel_size =4, strides = 2, padding = 'same',
                       activation = keras.layers.LeakyReLU(0.2))(inp_)
d = keras.layers.Dropout(0.4)(d)
d = keras.layers.Conv2D(64, kernel_size=4, strides = 2, padding='same',
                       activation = keras.layers.LeakyReLU(0.2))(d)
d = keras.layers.Dropout(0.4)(d)
d = keras.layers.Conv2D(128, kernel_size=4, strides = 2, padding='same',
                       activation = keras.layers.LeakyReLU(0.2))(d)
d = keras.layers.Dropout(0.4)(d)
d = keras.layers.Conv2D(256, kernel_size=4, strides = 2, padding='same',
                       activation = keras.layers.LeakyReLU(0.2))(d)
d = keras.layers.Dropout(0.4)(d)
d = keras.layers.Conv2D(512, kernel_size=4, strides = 2, padding='same',
                       activation = keras.layers.LeakyReLU(0.2))(d)
d = keras.layers.Dropout(0.4)(d)
d = keras.layers.Flatten()(d)
d = keras.layers.Dense(1, activation = 'sigmoid')(d)

discriminator = keras.models.Model(inputs = inp_, outputs = d)

# problem occurs here
gan = keras.models.Sequential([generator, discriminator])

到目前为止，互联网上没有任何解决方案对我有帮助

AssertionError                            Traceback (most recent call last)

<ipython-input-32-fd5633c3585a> in <module>()
----> 1 gan = keras.models.Sequential([generator, discriminator])

7 frames

/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
    455     self._self_setattr_tracking = False  # pylint: disable=protected-access
    456     try:
--> 457       result = method(self, *args, **kwargs)
    458     finally:
    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py in __init__(self, layers, name)
    140         layers = [layers]
    141       for layer in layers:
--> 142         self.add(layer)
    143 
    144   @property

/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
    455     self._self_setattr_tracking = False  # pylint: disable=protected-access
    456     try:
--> 457       result = method(self, *args, **kwargs)
    458     finally:
    459       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py in add(self, layer)
    204           # and create the node connecting the current layer
    205           # to the input layer we just created.
--> 206           layer(x)
    207           set_inputs = True
    208 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
    924     if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
    925       return self._functional_construction_call(inputs, args, kwargs,
--> 926                                                 input_list)
    927 
    928     # Maintains info about the `Layer.call` stack.

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
   1115           try:
   1116             with ops.enable_auto_cast_variables(self._compute_dtype_object):
-> 1117               outputs = call_fn(cast_inputs, *args, **kwargs)
   1118 
   1119           except errors.OperatorNotAllowedInGraphError as e:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py in call(self, inputs, training, mask)
    384     """
    385     return self._run_internal_graph(
--> 386         inputs, training=training, mask=mask)
    387 
    388   def compute_output_shape(self, input_shape):

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py in _run_internal_graph(self, inputs, training, mask)
    515     for x in self.outputs:
    516       x_id = str(id(x))
--> 517       assert x_id in tensor_dict, 'Could not compute output ' + str(x)
    518       output_tensors.append(tensor_dict[x_id].pop())
    519 

AssertionError: Could not compute output Tensor("conv2d_16/BiasAdd:0", shape=(None, 64, 64, 3), dtype=float32)

标签： tensorflowmachine-learningkerasdeep-learninggenerative-adversarial-network