我正在尝试融合两个形状为 (299, 299, 3), (224, 224, 3) 的图像输入的特征，但出现了形状错误。
这是我的代码
from tensorFlow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.vgg16 import VGG16
import tensorflow as tf
from tensorflow.keras import layers, Input
inp_pre_trained_model = InceptionV3( include_top=False)
inp_pre_trained_model.trainable=False
inp_input=tf.keras.Input(shape=(299,299,3),name="input_layer_inception_V3")
inp_x=inp_pre_trained_model (inp_input)
inp_x=layers.GlobalAveragePooling2D(name="global_average_pooling_layer_inception_v3")(inp_x)
vgg_pre_trained_model = VGG16( include_top=False)
vgg_pre_trained_model.trainable=False
vgg_input=tf.keras.Input(shape=(224,224,3),name="input_layer_VGG_16")
vgg_x=vgg_pre_trained_model(vgg_input)
vgg_x=layers.GlobalAveragePooling2D(name="global_average_pooling_layer_vgg_16")(vgg_x)
x=tf.keras.layers.concatenate([inp_x,vgg_x],axis=-1)
x = tf.keras.layers.Flatten()(x)
outputs=tf.keras.layers.Dense(5,activation="softmax", name= "output_layer") (x)
model=tf.keras.Model(inputs=[inp_input,vgg_input],outputs=outputs)
model.summary()
我的模型总结
Model: "model_9"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_layer_inception_V3 (Inpu [(None, 224, 224, 3 0 []
tLayer) )]
input_layer_VGG_16 (InputLayer [(None, 299, 299, 3 0 []
) )]
inception_v3 (Functional) (None, None, None, 21802784 ['input_layer_inception_V3[0][0]'
2048) ]
vgg16 (Functional) (None, None, None, 14714688 ['input_layer_VGG_16[0][0]']
512)
global_average_pooling_incepti (None, 2048) 0 ['inception_v3[0][0]']
on (GlobalAveragePooling2D)
global_average_pooling_vgg (Gl (None, 512) 0 ['vgg16[0][0]']
obalAveragePooling2D)
concatenate_71 (Concatenate) (None, 2560) 0 ['global_average_pooling_inceptio
n[0][0]',
'global_average_pooling_vgg[0][0
]']
output_layer (Dense) (None, 5) 12805 ['concatenate_71[0][0]']
==================================================================================================
Total params: 36,530,277
Trainable params: 12,805
Non-trainable params: 36,517,472
编译器
model.compile(loss="sparse_categorical_crossentropy",optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),metrics=["accuracy"])
train = tf.data.Dataset.zip((cache_train_data, ceced_train_data))
test = tf.data.Dataset.zip((cache_test_data, ceced_test_data))
train_dataset = train.prefetch(tf.data.AUTOTUNE)
test_dataset = test.prefetch(tf.data.AUTOTUNE)
train_dataset, test_dataset
--->(
适合模型
model_history = model.fit(train_dataset,
steps_per_epoch=len(train_dataset),
epochs=3,
validation_data=test_dataset,
validation_steps=len(test_dataset))
错误 ValueError：在用户代码中：
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1051, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1040, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1030, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 889, in train_step
y_pred = self(x, training=True)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 264, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" is '
ValueError: Input 1 of layer "model_9" is incompatible with the layer: expected shape=(None, 299, 299, 3), found shape=(None, 5)
您的拟合函数存在与训练数据相关的问题。从 Keras documentation 来看，
fitModel.fit(
x=None,
y=None,
batch_size=None,
epochs=1,
verbose="auto",
callbacks=None,
validation_split=0.0,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
validation_batch_size=None,
validation_freq=1,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
)
但是你传递的是一个 train_dataset，我认为它是一个将
tf.data.Datasetx_trainy_train为了修复错误，您应该将
xy我认为像this这样的事情应该做：
for images, labels in train_dataset.take(-1):
X_train = images.numpy()
y_train = labels.numpy()
# doing the same for validation
for images, labels in test_dataset.take(-1):
X_test = images.numpy()
y_test = labels.numpy()
你想要这样的东西：
model_history = model.fit(x=X_train, y=y_train, steps_per_epoch=len(train_dataset), epochs=3, validation_data=(X_test, y_test), validation_steps=len(test_dataset))