GANのInputShapeでのエラー

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-9-86703137e7b2> in <cell line: 33>()
     31 generator = build_generator()
     32 discriminator = build_discriminator()
---> 33 gan = build_gan(generator, discriminator)
     34 
     35 print("GANモデルが正常に構築され、コンパイルされました。")

2 frames
<ipython-input-9-86703137e7b2> in build_gan(generator, discriminator)
     23     label = Input(shape=(1,), dtype='int32')
     24     img = generator([noise, label])
---> 25     validity = discriminator([img, label])
     26     combined = Model([noise, label], validity)
     27     combined.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))

/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py in error_handler(*args, **kwargs)
     68             # To get the full stack trace, call:
     69             # `tf.debugging.disable_traceback_filtering()`
---> 70             raise e.with_traceback(filtered_tb) from None
     71         finally:
     72             del filtered_tb

/usr/local/lib/python3.10/dist-packages/keras/src/engine/input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name)
    296                 if spec_dim is not None and dim is not None:
    297                     if spec_dim != dim:
--> 298                         raise ValueError(
    299                             f'Input {input_index} of layer "{layer_name}" is '
    300                             "incompatible with the layer: "

ValueError: Exception encountered when calling layer "model_1" (type Functional).

Input 0 of layer "sequential_1" is incompatible with the layer: expected shape=(None, 32, 32, 3), found shape=(None, 3072)

Call arguments received by layer "model_1" (type Functional):
  • inputs=['tf.Tensor(shape=(None, 32, 32, 3), dtype=float32)', 'tf.Tensor(shape=(None, 1), dtype=int32)']
  • training=None
  • mask=None

import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout, BatchNormalization, LeakyReLU, Embedding, multiply
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.optimizers import Adam

# CIFAR-10データセットの読み込み
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# データの正規化
x_train = (x_train.astype(np.float32) - 127.5) / 127.5
x_test = (x_test.astype(np.float32) - 127.5) / 127.5
train_data = np.vstack((x_train, x_test))

# パラメータの設定
img_shape = (32, 32, 3)
latent_dim = 100
num_classes = 10

# ジェネレータの構築
def build_generator():
   model = Sequential()
   model.add(Dense(256, input_dim=latent_dim))
   model.add(LeakyReLU(alpha=0.2))
   model.add(BatchNormalization(momentum=0.8))
   model.add(Dense(512))
   model.add(LeakyReLU(alpha=0.2))
   model.add(BatchNormalization(momentum=0.8))
   model.add(Dense(1024))
   model.add(LeakyReLU(alpha=0.2))
   model.add(BatchNormalization(momentum=0.8))
   model.add(Dense(np.prod(img_shape), activation='tanh'))
   model.add(Reshape(img_shape))
   noise = Input(shape=(latent_dim,))
   label = Input(shape=(1,), dtype='int32')
   label_embedding = Flatten()(Embedding(num_classes, latent_dim)(label))
   model_input = multiply([noise, label_embedding])
   img = model(model_input)
   return Model([noise, label], img)

   # ディスクリミネータの構築
def build_discriminator():
   model = Sequential()
   model.add(Flatten(input_shape=img_shape))
   model.add(Dense(512))
   model.add(LeakyReLU(alpha=0.2))
   model.add(Dense(256))
   model.add(LeakyReLU(alpha=0.2))
   model.add(Dense(1, activation='sigmoid'))
   img = Input(shape=img_shape)
   label = Input(shape=(1,), dtype='int32')
   label_embedding = Flatten()(Embedding(num_classes, np.prod(img_shape))(label))
   flat_img = Flatten()(img)
   model_input = multiply([flat_img, label_embedding])
   validity = model(model_input)
   return Model([img, label], validity)

   # GANの構築
def build_gan(generator, discriminator):
    discriminator.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5), metrics=['accuracy'])
    discriminator.trainable = False
    noise = Input(shape=(latent_dim,))
    label = Input(shape=(1,), dtype='int32')
    img = generator([noise, label])
    validity = discriminator([img, label])
    combined = Model([noise, label], validity)
    combined.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))
    return combined

# モデルの初期化
generator = build_generator()
discriminator = build_discriminator()
gan = build_gan(generator, discriminator)