I've directory structure like this, for my dataset:
|--train
|--test
|--valid
In the train folder, these are pairs of images like xyz_sat and xyz_mask.
So I've loaded them with Pillow, and converted them to NumPy array to feed to TensorFlow:
train_sat = [np.array(Image.open(name),dtype="float32") for name in train_names_sat]
train_mask = [np.array(Image.open(name).convert('RGB'),dtype="float32") for name in train_names_mask]
Then normalized them and all other things.
I'm trying to feed images to my model like this:
history = model.fit(train_sat,
train_mask,
validation_split = 0.15,
epochs=EPOCHS,
batch_size = BATCH_SIZE,
#callbacks = [callbacks]
)
But I'm getting this error:
ValueError: Layer model_4 expects 1 input(s), but it received 10 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 1024, 3) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(None, 1024, 3) dtype=float32>, <tf.Tensor 'IteratorGetNext:2' shape=(None, 1024, 3) dtype=float32>, <tf.Tensor 'IteratorGetNext:3' shape=(None, 1024, 3) dtype=float32>, <tf.Tensor 'IteratorGetNext:4' shape=(None, 1024, 3) dtype=float32>, <tf.Tensor 'IteratorGetNext:5' shape=(None, 1024, 3) dtype=float32>, <tf.Tensor 'IteratorGetNext:6' shape=(None, 1024, 3) dtype=float32>, <tf.Tensor 'IteratorGetNext:7' shape=(None, 1024, 3) dtype=float32>, <tf.Tensor 'IteratorGetNext:8' shape=(None, 1024, 3) dtype=float32>, <tf.Tensor 'IteratorGetNext:9' shape=(None, 1024, 3) dtype=float32>]
Please share how you debugged the error, that would be more helpful.
I can understand that it's because I've 10 images in my dataset training set, for testing purposes, they are causing the error.
My UNet model looks like this:
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_5 (InputLayer) [(None, 1024, 1024, 0
__________________________________________________________________________________________________
conv2d_112 (Conv2D) (None, 1024, 1024, 6 1792 input_5[0][0]
__________________________________________________________________________________________________
batch_normalization_124 (BatchN (None, 1024, 1024, 6 256 conv2d_112[0][0]
__________________________________________________________________________________________________
conv2d_113 (Conv2D) (None, 1024, 1024, 6 36928 batch_normalization_124[0][0]
__________________________________________________________________________________________________
batch_normalization_125 (BatchN (None, 1024, 1024, 6 256 conv2d_113[0][0]
__________________________________________________________________________________________________
conv2d_114 (Conv2D) (None, 1024, 1024, 6 36928 batch_normalization_125[0][0]
__________________________________________________________________________________________________
max_pooling2d_20 (MaxPooling2D) (None, 512, 512, 64) 0 conv2d_114[0][0]
__________________________________________________________________________________________________
batch_normalization_126 (BatchN (None, 512, 512, 64) 256 max_pooling2d_20[0][0]
__________________________________________________________________________________________________
conv2d_115 (Conv2D) (None, 512, 512, 64) 36928 batch_normalization_126[0][0]
__________________________________________________________________________________________________
batch_normalization_127 (BatchN (None, 512, 512, 64) 256 conv2d_115[0][0]
__________________________________________________________________________________________________
conv2d_116 (Conv2D) (None, 512, 512, 64) 36928 batch_normalization_127[0][0]
__________________________________________________________________________________________________
batch_normalization_128 (BatchN (None, 512, 512, 64) 256 conv2d_116[0][0]
__________________________________________________________________________________________________
conv2d_117 (Conv2D) (None, 512, 512, 64) 36928 batch_normalization_128[0][0]
__________________________________________________________________________________________________
max_pooling2d_21 (MaxPooling2D) (None, 256, 256, 64) 0 conv2d_117[0][0]
__________________________________________________________________________________________________
batch_normalization_129 (BatchN (None, 256, 256, 64) 256 max_pooling2d_21[0][0]
__________________________________________________________________________________________________
conv2d_118 (Conv2D) (None, 256, 256, 64) 36928 batch_normalization_129[0][0]
__________________________________________________________________________________________________
batch_normalization_130 (BatchN (None, 256, 256, 64) 256 conv2d_118[0][0]
__________________________________________________________________________________________________
conv2d_119 (Conv2D) (None, 256, 256, 64) 36928 batch_normalization_130[0][0]
__________________________________________________________________________________________________
batch_normalization_131 (BatchN (None, 256, 256, 64) 256 conv2d_119[0][0]
__________________________________________________________________________________________________
conv2d_120 (Conv2D) (None, 256, 256, 64) 36928 batch_normalization_131[0][0]
__________________________________________________________________________________________________
max_pooling2d_22 (MaxPooling2D) (None, 128, 128, 64) 0 conv2d_120[0][0]
__________________________________________________________________________________________________
batch_normalization_132 (BatchN (None, 128, 128, 64) 256 max_pooling2d_22[0][0]
__________________________________________________________________________________________________
conv2d_121 (Conv2D) (None, 128, 128, 64) 36928 batch_normalization_132[0][0]
__________________________________________________________________________________________________
batch_normalization_133 (BatchN (None, 128, 128, 64) 256 conv2d_121[0][0]
__________________________________________________________________________________________________
conv2d_122 (Conv2D) (None, 128, 128, 64) 36928 batch_normalization_133[0][0]
__________________________________________________________________________________________________
batch_normalization_134 (BatchN (None, 128, 128, 64) 256 conv2d_122[0][0]
__________________________________________________________________________________________________
conv2d_123 (Conv2D) (None, 128, 128, 64) 36928 batch_normalization_134[0][0]
__________________________________________________________________________________________________
max_pooling2d_23 (MaxPooling2D) (None, 64, 64, 64) 0 conv2d_123[0][0]
__________________________________________________________________________________________________
batch_normalization_135 (BatchN (None, 64, 64, 64) 256 max_pooling2d_23[0][0]
__________________________________________________________________________________________________
conv2d_124 (Conv2D) (None, 64, 64, 64) 36928 batch_normalization_135[0][0]
__________________________________________________________________________________________________
batch_normalization_136 (BatchN (None, 64, 64, 64) 256 conv2d_124[0][0]
__________________________________________________________________________________________________
conv2d_125 (Conv2D) (None, 64, 64, 64) 36928 batch_normalization_136[0][0]
__________________________________________________________________________________________________
batch_normalization_137 (BatchN (None, 64, 64, 64) 256 conv2d_125[0][0]
__________________________________________________________________________________________________
conv2d_126 (Conv2D) (None, 64, 64, 64) 36928 batch_normalization_137[0][0]
__________________________________________________________________________________________________
max_pooling2d_24 (MaxPooling2D) (None, 32, 32, 64) 0 conv2d_126[0][0]
__________________________________________________________________________________________________
batch_normalization_138 (BatchN (None, 32, 32, 64) 256 max_pooling2d_24[0][0]
__________________________________________________________________________________________________
conv2d_127 (Conv2D) (None, 32, 32, 64) 36928 batch_normalization_138[0][0]
__________________________________________________________________________________________________
batch_normalization_139 (BatchN (None, 32, 32, 64) 256 conv2d_127[0][0]
__________________________________________________________________________________________________
conv2d_128 (Conv2D) (None, 32, 32, 64) 36928 batch_normalization_139[0][0]
__________________________________________________________________________________________________
batch_normalization_140 (BatchN (None, 32, 32, 64) 256 conv2d_128[0][0]
__________________________________________________________________________________________________
conv2d_transpose_20 (Conv2DTran (None, 64, 64, 64) 36928 batch_normalization_140[0][0]
__________________________________________________________________________________________________
concatenate_20 (Concatenate) (None, 64, 64, 128) 0 conv2d_transpose_20[0][0]
conv2d_125[0][0]
__________________________________________________________________________________________________
batch_normalization_141 (BatchN (None, 64, 64, 128) 512 concatenate_20[0][0]
__________________________________________________________________________________________________
conv2d_129 (Conv2D) (None, 64, 64, 96) 110688 batch_normalization_141[0][0]
__________________________________________________________________________________________________
batch_normalization_142 (BatchN (None, 64, 64, 96) 384 conv2d_129[0][0]
__________________________________________________________________________________________________
conv2d_130 (Conv2D) (None, 64, 64, 64) 55360 batch_normalization_142[0][0]
__________________________________________________________________________________________________
batch_normalization_143 (BatchN (None, 64, 64, 64) 256 conv2d_130[0][0]
__________________________________________________________________________________________________
conv2d_transpose_21 (Conv2DTran (None, 128, 128, 64) 36928 batch_normalization_143[0][0]
__________________________________________________________________________________________________
concatenate_21 (Concatenate) (None, 128, 128, 128 0 conv2d_transpose_21[0][0]
conv2d_122[0][0]
__________________________________________________________________________________________________
batch_normalization_144 (BatchN (None, 128, 128, 128 512 concatenate_21[0][0]
__________________________________________________________________________________________________
conv2d_131 (Conv2D) (None, 128, 128, 96) 110688 batch_normalization_144[0][0]
__________________________________________________________________________________________________
batch_normalization_145 (BatchN (None, 128, 128, 96) 384 conv2d_131[0][0]
__________________________________________________________________________________________________
conv2d_132 (Conv2D) (None, 128, 128, 64) 55360 batch_normalization_145[0][0]
__________________________________________________________________________________________________
batch_normalization_146 (BatchN (None, 128, 128, 64) 256 conv2d_132[0][0]
__________________________________________________________________________________________________
conv2d_transpose_22 (Conv2DTran (None, 256, 256, 64) 36928 batch_normalization_146[0][0]
__________________________________________________________________________________________________
concatenate_22 (Concatenate) (None, 256, 256, 128 0 conv2d_transpose_22[0][0]
conv2d_119[0][0]
__________________________________________________________________________________________________
batch_normalization_147 (BatchN (None, 256, 256, 128 512 concatenate_22[0][0]
__________________________________________________________________________________________________
conv2d_133 (Conv2D) (None, 256, 256, 96) 110688 batch_normalization_147[0][0]
__________________________________________________________________________________________________
batch_normalization_148 (BatchN (None, 256, 256, 96) 384 conv2d_133[0][0]
__________________________________________________________________________________________________
conv2d_134 (Conv2D) (None, 256, 256, 64) 55360 batch_normalization_148[0][0]
__________________________________________________________________________________________________
batch_normalization_149 (BatchN (None, 256, 256, 64) 256 conv2d_134[0][0]
__________________________________________________________________________________________________
conv2d_transpose_23 (Conv2DTran (None, 512, 512, 64) 36928 batch_normalization_149[0][0]
__________________________________________________________________________________________________
concatenate_23 (Concatenate) (None, 512, 512, 128 0 conv2d_transpose_23[0][0]
conv2d_116[0][0]
__________________________________________________________________________________________________
batch_normalization_150 (BatchN (None, 512, 512, 128 512 concatenate_23[0][0]
__________________________________________________________________________________________________
conv2d_135 (Conv2D) (None, 512, 512, 96) 110688 batch_normalization_150[0][0]
__________________________________________________________________________________________________
batch_normalization_151 (BatchN (None, 512, 512, 96) 384 conv2d_135[0][0]
__________________________________________________________________________________________________
conv2d_136 (Conv2D) (None, 512, 512, 64) 55360 batch_normalization_151[0][0]
__________________________________________________________________________________________________
batch_normalization_152 (BatchN (None, 512, 512, 64) 256 conv2d_136[0][0]
__________________________________________________________________________________________________
conv2d_transpose_24 (Conv2DTran (None, 1024, 1024, 6 36928 batch_normalization_152[0][0]
__________________________________________________________________________________________________
concatenate_24 (Concatenate) (None, 1024, 1024, 1 0 conv2d_transpose_24[0][0]
conv2d_113[0][0]
__________________________________________________________________________________________________
batch_normalization_153 (BatchN (None, 1024, 1024, 1 512 concatenate_24[0][0]
__________________________________________________________________________________________________
conv2d_137 (Conv2D) (None, 1024, 1024, 9 110688 batch_normalization_153[0][0]
__________________________________________________________________________________________________
batch_normalization_154 (BatchN (None, 1024, 1024, 9 384 conv2d_137[0][0]
__________________________________________________________________________________________________
conv2d_138 (Conv2D) (None, 1024, 1024, 6 55360 batch_normalization_154[0][0]
__________________________________________________________________________________________________
conv2d_139 (Conv2D) (None, 1024, 1024, 1 65 conv2d_138[0][0]
==================================================================================================
Total params: 1,617,441
Trainable params: 1,612,513
Non-trainable params: 4,928