tensorflow - 无法通过 GAN 模型处理数据

#Create discriminator Model def make_discriminator_model(): model = tf.keras.Sequential() model.add(tf.keras.layers.Conv2D(7,(3,3),padding="same",input_shape=(28,28,1))) model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.LeakyReLU()) model.add(tf.keras.layers.Dense(50,activation="relu")) model.add(tf.keras.layers.Dense(1)) return model #Test Model model_discriminator = make_discriminator_model() model_discriminator(np.random.rand(1,28,28,1).astype("float32")) #Apply optimiser discriminator_optimiser = tf.optimizers.Adam(1e-3) #Calc discriminator loss def get_discriminator_loss(real_predictions, fake_predictions): real_predictions = tf.sigmoid(real_predictions) fake_predictions = tf.sigmoid(fake_predictions) real_loss = tf.losses.binary_crossentropy(tf.ones_like(real_predictions),real_predictions) fake_loss = tf.losses.binary_crossentropy(tf.zeros_like(fake_predictions),fake_predictions) return fake_loss+real_loss #Create generator model def make_generator_model(): model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(7*7*256,input_shape = (100,))) model.add(tf.keras.layers.BatchNormalization()) model.add(tf.keras.layers.Reshape((7,7,256))) model.add(tf.keras.layers.Conv2DTranspose(128,(3,3),padding="same")) model.add(tf.keras.layers.BatchNormalization()) model.add(tf.keras.layers.Conv2DTranspose(64,(3,3),strides=(2,2),padding="same")) model.add(tf.keras.layers.BatchNormalization()) model.add(tf.keras.layers.Conv2DTranspose(1,(3,3),strides=(2,2),padding="same")) return model #Test model generator = make_generator_model() #Apply optimiser generator_optimiser = tf.optimizers.Adam(1e-4) #Calculate generator loss def get_generator_loss(fake_predictions): fake_predictions = tf.sigmoid(fake_predictions) fake_loss = tf.losses.binary_crossentropy(tf.ones_like(fake_predictions),fake_predictions) return fake_loss

#Create discriminator Model def make_discriminator_model(): model = tf.keras.Sequential() model.add(tf.keras.layers.Conv2D(7,(3,3),padding="same",input_shape=(180,180,1))) model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.LeakyReLU()) model.add(tf.keras.layers.Dense(50,activation="relu")) model.add(tf.keras.layers.Dense(1)) return model #Test Model model_discriminator = make_discriminator_model() model_discriminator(np.random.rand(1,180,180,1).astype("float32")) #Apply optimiser discriminator_optimiser = tf.optimizers.Adam(1e-3) #Calc discriminator loss def get_discriminator_loss(real_predictions, fake_predictions): real_predictions = tf.sigmoid(real_predictions) fake_predictions = tf.sigmoid(fake_predictions) real_loss = tf.losses.binary_crossentropy(tf.ones_like(real_predictions),real_predictions) fake_loss = tf.losses.binary_crossentropy(tf.zeros_like(fake_predictions),fake_predictions) return fake_loss+real_loss #Create generator model def make_generator_model(): model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(7*7*256,input_shape = (100,))) model.add(tf.keras.layers.BatchNormalization()) model.add(tf.keras.layers.Reshape((7,7,256))) model.add(tf.keras.layers.Conv2DTranspose(128,(3,3),padding="same")) model.add(tf.keras.layers.BatchNormalization()) model.add(tf.keras.layers.Conv2DTranspose(64,(3,3),strides=(2,2),padding="same")) model.add(tf.keras.layers.BatchNormalization()) model.add(tf.keras.layers.Conv2DTranspose(1,(3,3),strides=(2,2),padding="same")) return model #Test model generator = make_generator_model() #Apply optimiser generator_optimiser = tf.optimizers.Adam(1e-4) #Calculate generator loss def get_generator_loss(fake_predictions): fake_predictions = tf.sigmoid(fake_predictions) fake_loss = tf.losses.binary_crossentropy(tf.ones_like(fake_predictions),fake_predictions) return fake_loss

def train(dataset, epochs): for _ in range(epochs): for images in dataset: images = tf.cast(images,tf.dtypes.float32) train_step(images) def train_step(images): fake_image_noise = np.random.randn(1,100).astype("float32") with tf.GradientTape() as gen_tape,tf.GradientTape() as disc_tape: generated_images = generator(fake_image_noise) real_output = model_discriminator(images) fake_output = model_discriminator(generated_images) gen_loss = get_generator_loss(fake_output) disc_loss = get_discriminator_loss(real_output,fake_output) gradient_of_generator = gen_tape.gradient(gen_loss,generator.trainable_variables) gradient_of_discriminator = disc_tape.gradient(disc_loss,model_discriminator.trainable_variables) generator_optimiser.apply_gradients(zip(gradient_of_generator,generator.trainable_variables)) discriminator_optimiser.apply_gradients(zip(gradient_of_discriminator,model_discriminator.trainable_variables)) print('generator loss: ', np.mean(gen_loss)) print('discriminator loss:', np.mean(disc_loss))

--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-75-422a2dcc2f99> in <module>() ----> 1 train(train_dataset, 20) 7 frames <ipython-input-69-0628a680d6f7> in train(dataset, epochs) 3 for images in dataset: 4 images = tf.cast(images,tf.dtypes.float32) ----> 5 train_step(images) <ipython-input-70-b481f5bf591c> in train_step(images) 3 with tf.GradientTape() as gen_tape,tf.GradientTape() as disc_tape: 4 generated_images = generator(fake_image_noise) ----> 5 real_output = model_discriminator(images) 6 fake_output = model_discriminator(generated_images) 7 /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs) 1028 with autocast_variable.enable_auto_cast_variables( 1029 self._compute_dtype_object): -> 1030 outputs = call_fn(inputs, *args, **kwargs) 1031 1032 if self._activity_regularizer: /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py in call(self, inputs, training, mask) 378 if not self.built: 379 self._init_graph_network(self.inputs, self.outputs) --> 380 return super(Sequential, self).call(inputs, training=training, mask=mask) 381 382 outputs = inputs # handle the corner case where self.layers is empty /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py in call(self, inputs, training, mask) 419 """ 420 return self._run_internal_graph( --> 421 inputs, training=training, mask=mask) 422 423 def compute_output_shape(self, input_shape): /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py in _run_internal_graph(self, inputs, training, mask) 554 555 args, kwargs = node.map_arguments(tensor_dict) --> 556 outputs = node.layer(*args, **kwargs) 557 558 # Update tensor_dict. /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs) 1011 training=training_mode): 1012 -> 1013 input_spec.assert_input_compatibility(self.input_spec, inputs, self.name) 1014 if eager: 1015 call_fn = self.call /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name) 253 ' incompatible with the layer: expected axis ' + str(axis) + 254 ' of input shape to have value ' + str(value) + --> 255 ' but received input with shape ' + display_shape(x.shape)) 256 # Check shape. 257 if spec.shape is not None and shape.rank is not None: ValueError: Input 0 of layer dense_7 is incompatible with the layer: expected axis -1 of input shape to have value 226800 but received input with shape (1, 5488)