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HenryAI
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KerasCodeExamples.txt

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self.pos_embed = layers.Embedding(input_dim=num_patch, output_dim=embed_dim)
def call(self, patch):
pos = tf.range(start=0, limit=self.num_patch, delta=1)
return self.proj(patch) + self.pos_embed(pos)
class PatchMerging(tf.keras.layers.Layer):
def __init__(self, num_patch, embed_dim):
super(PatchMerging, self).__init__()
self.num_patch = num_patch
self.embed_dim = embed_dim
self.linear_trans = layers.Dense(2 * embed_dim, use_bias=False)
def call(self, x):
height, width = self.num_patch
_, _, C = x.get_shape().as_list()
x = tf.reshape(x, shape=(-1, height, width, C))
x0 = x[:, 0::2, 0::2, :]
x1 = x[:, 1::2, 0::2, :]
x2 = x[:, 0::2, 1::2, :]
x3 = x[:, 1::2, 1::2, :]
x = tf.concat((x0, x1, x2, x3), axis=-1)
x = tf.reshape(x, shape=(-1, (height // 2) * (width // 2), 4 * C))
return self.linear_trans(x)
Build the model
We put together the Swin Transformer model.
input = layers.Input(input_shape)
x = layers.RandomCrop(image_dimension, image_dimension)(input)
x = layers.RandomFlip(\"horizontal\")(x)
x = PatchExtract(patch_size)(x)
x = PatchEmbedding(num_patch_x * num_patch_y, embed_dim)(x)
x = SwinTransformer(
dim=embed_dim,
num_patch=(num_patch_x, num_patch_y),
num_heads=num_heads,
window_size=window_size,
shift_size=0,
num_mlp=num_mlp,
qkv_bias=qkv_bias,
dropout_rate=dropout_rate,
)(x)
x = SwinTransformer(
dim=embed_dim,
num_patch=(num_patch_x, num_patch_y),
num_heads=num_heads,
window_size=window_size,
shift_size=shift_size,
num_mlp=num_mlp,
qkv_bias=qkv_bias,
dropout_rate=dropout_rate,
)(x)
x = PatchMerging((num_patch_x, num_patch_y), embed_dim=embed_dim)(x)
x = layers.GlobalAveragePooling1D()(x)
output = layers.Dense(num_classes, activation=\"softmax\")(x)
2021-09-13 08:03:19.266695: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-13 08:03:19.275199: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-13 08:03:19.275997: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-13 08:03:19.277483: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-09-13 08:03:19.278433: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-13 08:03:19.279102: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-13 08:03:19.279706: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-13 08:03:21.258771: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-13 08:03:21.259481: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-13 08:03:21.260191: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-09-13 08:03:21.261723: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14684 MB memory: -> device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:04.0, compute capability: 7.0
Train on CIFAR-100
We train the model on CIFAR-100. Here, we only train the model for 40 epochs to keep the training time short in this example. In practice, you should train for 150 epochs to reach convergence.
model = keras.Model(input, output)
model.compile(
loss=keras.losses.CategoricalCrossentropy(label_smoothing=label_smoothing),
optimizer=tfa.optimizers.AdamW(
learning_rate=learning_rate, weight_decay=weight_decay
),
metrics=[
keras.metrics.CategoricalAccuracy(name=\"accuracy\"),
keras.metrics.TopKCategoricalAccuracy(5, name=\"top-5-accuracy\"),
],
)
history = model.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=num_epochs,
validation_split=validation_split,
)
2021-09-13 08:03:23.935873: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
Epoch 1/40
352/352 [==============================] - 19s 34ms/step - loss: 4.1679 - accuracy: 0.0817 - top-5-accuracy: 0.2551 - val_loss: 3.8964 - val_accuracy: 0.1242 - val_top-5-accuracy: 0.3568
Epoch 2/40
352/352 [==============================] - 11s 32ms/step - loss: 3.7278 - accuracy: 0.1617 - top-5-accuracy: 0.4246 - val_loss: 3.6518 - val_accuracy: 0.1756 - val_top-5-accuracy: 0.4580
Epoch 3/40
352/352 [==============================] - 11s 32ms/step - loss: 3.5245 - accuracy: 0.2077 - top-5-accuracy: 0.4946 - val_loss: 3.4609 - val_accuracy: 0.2248 - val_top-5-accuracy: 0.5222
Epoch 4/40
352/352 [==============================] - 11s 32ms/step - loss: 3.3856 - accuracy: 0.2408 - top-5-accuracy: 0.5430 - val_loss: 3.3515 - val_accuracy: 0.2514 - val_top-5-accuracy: 0.5540