Подскажите, пожалуйста как решить эту проблему? Если нужно могу приложить Jupyter Notebook и папку с файлами(там изображения).
def define_discriminator(in_shape = (106, 106, 1)):
model = Sequential()
model.add(Conv2D(64, (3,3), strides = (2,2), padding = "same", input_shape = in_shape))
model.add(LeakyReLU(alpha = 0.2))
model.add(Dropout(0.5))
model.add(Conv2D(64, (3,3), strides = (2,2), padding = "same"))
model.add(LeakyReLU(alpha = 0.2))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(1, activation = "sigmoid"))
opt = Adam(learning_rate = 0.0002, beta_1 = 0.5)
model.compile(loss = "binary_crossentropy", optimizer = opt, metrics = ["accuracy"])
return model
def define_generator(latent_dim):
model = Sequential()
n_nodes = 128 * 53 * 53
model.add(Dense(n_nodes, input_dim = latent_dim))
model.add(LeakyReLU(alpha = 0.2))
model.add(Reshape((53, 53, 128)))
model.add(Dense(1024))
model.add(Conv2DTranspose(1024, (4,4), strides = (2,2), padding = "same"))
model.add(Dense(1024))
model.add(LeakyReLU(alpha = 0.2))
model.add(Dense(1024))
model.add(Conv2D(1, (7,7), padding = "same", activation = "sigmoid"))
return model
def define_gan(g_model, d_model):
d_model.trianabel = False
model = Sequential()
model.add(g_model)
model.add(d_model)
opt = Adam(learning_rate = 0.0002, beta_1 = 0.5)
model.compile(loss = "binary_crossentropy", optimizer = opt)
return model
def generate_real_samples(dataset, n_samples):
ix = randint(0, dataset.shape[0], n_samples)
X = dataset[ix].T
Y = ones((n_samples, 1)).T
return X, Y
def generate_latent_points(latent_dim, n_samples):
x_input = randn(latent_dim * n_samples)
x_input = x_input.reshape(n_samples, latent_dim)
return x_input
def generate_fake_samples(g_model, latent_dim, n_samples):
x_input = generate_latent_points(latent_dim, n_samples)
X = g_model.predict(x_input).T
Y = zeros((n_samples, 1)).T
return X, Y
import tensorflow as tf
def train(g_model, d_model, gan_model, dataset, latent_dim, n_epochs=51, n_batch=10):
bat_per_epo = int(dataset.shape[0] / n_batch)
half_batch = int(n_batch / 2)
for i in range(n_epochs):
for j in range(bat_per_epo):
X_real, y_real = generate_real_samples(dataset, half_batch)
X_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)
print(X_real, X_fake)
print(y_real, y_fake)
X, y = vstack((X_real, X_fake)), vstack((y_real, y_fake))
d_loss, _ = d_model.train_on_batch(X, y)
X_gan = generate_latent_points(latent_dim, n_batch)
y_gan = ones((n_batch, 1))
g_loss = gan_model.train_on_batch(X_gan, y_gan)
print('>%d, %d/%d, d=%.3f, g=%.3f' % (i+1, j+1, bat_per_epo, d_loss, g_loss))
if (i+1) % 10 == 0:
summarize_performance(i, g_model, d_model, dataset, latent_dim)
clear_output()
latent_dim = 100
d_model = define_discriminator()
g_model = define_generator(latent_dim)
gan_model = define_gan(g_model, d_model)
print(pixels.shape)
train(g_model, d_model, gan_model, np.array(pixels), latent_dim)
Код ошибки:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-611-b3360c520333> in <module>
4 gan_model = define_gan(g_model, d_model)
5 print(pixels.shape)
----> 6 train(g_model, d_model, gan_model, np.array(pixels), latent_dim)
<ipython-input-610-d7d51b905847> in train(g_model, d_model, gan_model, dataset, latent_dim,
n_epochs, n_batch)
13 print(X.shape)
14 print(y.shape)
---> 15 d_loss, _ = d_model.train_on_batch(X, y)
16 X_gan = generate_latent_points(latent_dim, n_batch)
17 y_gan = ones((n_batch, 1))
~\anaconda3\envs\LikeProject\lib\site-packages\keras\engine\training.py in
train_on_batch(self, x,
y, sample_weight, class_weight, reset_metrics, return_dict)
1854 class_weight)
1855 self.train_function = self.make_train_function()
-> 1856 logs = self.train_function(iterator)
1857
1858 if reset_metrics:
~\anaconda3\envs\LikeProject\lib\site-packages\tensorflow\python\eager\def_function.py in
__call__(self, *args, **kwds)
883
884 with OptionalXlaContext(self._jit_compile):
--> 885 result = self._call(*args, **kwds)
886
887 new_tracing_count = self.experimental_get_tracing_count()
~\anaconda3\envs\LikeProject\lib\site-packages\tensorflow\python\eager\def_function.py in
_call(self, *args, **kwds)
931 # This is the first call of __call__, so we have to initialize.
932 initializers = []
--> 933 self._initialize(args, kwds, add_initializers_to=initializers)
934 finally:
935 # At this point we know that the initialization is complete (or less
~\anaconda3\envs\LikeProject\lib\site-packages\tensorflow\python\eager\def_function.py in
_initialize(self, args, kwds, add_initializers_to)
757 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
758 self._concrete_stateful_fn = (
--> 759 self._stateful_fn._get_concrete_function_internal_garbage_collected( #
pylint:
disable=protected-access
760 *args, **kwds))
761
~\anaconda3\envs\LikeProject\lib\site-packages\tensorflow\python\eager\function.py in
_get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
3064 args, kwargs = None, None
3065 with self._lock:
-> 3066 graph_function, _ = self._maybe_define_function(args, kwargs)
3067 return graph_function
3068
~\anaconda3\envs\LikeProject\lib\site-packages\tensorflow\python\eager\function.py in
_maybe_define_function(self, args, kwargs)
3461
3462 self._function_cache.missed.add(call_context_key)
-> 3463 graph_function = self._create_graph_function(args, kwargs)
3464 self._function_cache.primary[cache_key] = graph_function
3465
~\anaconda3\envs\LikeProject\lib\site-packages\tensorflow\python\eager\function.py in
_create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3296 arg_names = base_arg_names + missing_arg_names
3297 graph_function = ConcreteFunction(
-> 3298 func_graph_module.func_graph_from_py_func(
3299 self._name,
3300 self._python_function,
~\anaconda3\envs\LikeProject\lib\site-packages\tensorflow\python\framework\func_graph.py in
func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph,
autograph_options, add_control_dependencies, arg_names, op_return_value, collections,
capture_by_value, override_flat_arg_shapes, acd_record_initial_resource_uses)
1005 _, original_func = tf_decorator.unwrap(python_func)
1006
-> 1007 func_outputs = python_func(*func_args, **func_kwargs)
1008
1009 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~\anaconda3\envs\LikeProject\lib\site-packages\tensorflow\python\eager\def_function.py in
wrapped_fn(*args, **kwds)
666 # the function a weak reference to itself to avoid a reference cycle.
667 with OptionalXlaContext(compile_with_xla):
--> 668 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
669 return out
670
~\anaconda3\envs\LikeProject\lib\site-packages\tensorflow\python\framework\func_graph.py in
wrapper(*args, **kwargs)
992 except Exception as e: # pylint:disable=broad-except
993 if hasattr(e, "ag_error_metadata"):
--> 994 raise e.ag_error_metadata.to_exception(e)
995 else:
996 raise
ValueError: in user code:
C:\Users\nefar\anaconda3\envs\LikeProject\lib\site-packages\keras\engine\training.py:853
train_function *
return step_function(self, iterator)
C:\Users\nefar\anaconda3\envs\LikeProject\lib\site-packages\keras\engine\training.py:842
step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\nefar\anaconda3\envs\LikeProject\lib\site-
packages\tensorflow\python\distribute\distribute_lib.py:1286 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\nefar\anaconda3\envs\LikeProject\lib\site-
packages\tensorflow\python\distribute\distribute_lib.py:2849 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\nefar\anaconda3\envs\LikeProject\lib\site-
packages\tensorflow\python\distribute\distribute_lib.py:3632 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\nefar\anaconda3\envs\LikeProject\lib\site-packages\keras\engine\training.py:835
run_step **
outputs = model.train_step(data)
C:\Users\nefar\anaconda3\envs\LikeProject\lib\site-packages\keras\engine\training.py:787
train_step
y_pred = self(x, training=True)
C:\Users\nefar\anaconda3\envs\LikeProject\lib\site-
packages\keras\engine\base_layer.py:1020
__call__
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
C:\Users\nefar\anaconda3\envs\LikeProject\lib\site-packages\keras\engine\input_spec.py:250
assert_input_compatibility
raise ValueError(
ValueError: Input 0 of layer sequential_414 is incompatible with the layer: expected axis
-1
of input shape to have value 1 but received input with shape (4, 106, 106, 5)
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