HDF5 file, superblock version 0
group /
	attribute backend = "tensorflow"
	attribute keras_version = "2.11.0"
	attribute model_config = "{\"class_name\": \"Functional\", \"config\": {\"name\": \"model\", \"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 32, 32, 3], \"dtype\": \"float32\", \"sparse\": false, \"ragged\": false, \"name\": \"input_1\"}, \"name\": \"input_1\", \"inbound_nodes\": []}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv2d\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 32, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv2d\", \"inbound_nodes\": [[[\"input_1\", 0, 0, {}]]]}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d\", \"trainable\": true, \"dtype\": \"float32\", \"pool_size\": [2, 2], \"padding\": \"same\", \"strides\": [2, 2], \"data_format\": \"channels_last\"}, \"name\": \"max_pooling2d\", \"inbound_nodes\": [[[\"conv2d\", 0, 0, {}]]]}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv2d_1\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 16, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv2d_1\", \"inbound_nodes\": [[[\"max_pooling2d\", 0, 0, {}]]]}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_1\", \"trainable\": true, \"dtype\": \"float32\", \"pool_size\": [2, 2], \"padding\": \"same\", \"strides\": [2, 2], \"data_format\": \"channels_last\"}, \"name\": \"max_pooling2d_1\", \"inbound_nodes\": [[[\"conv2d_1\", 0, 0, {}]]]}], \"input_layers\": [[\"input_1\", 0, 0]], \"output_layers\": [[\"max_pooling2d_1\", 0, 0]]}}"
	attribute training_config = "{\"loss\": \"mse\", \"metrics\": null, \"weighted_metrics\": null, \"loss_weights\": null, \"optimizer_config\": {\"class_name\": \"Custom>Adam\", \"config\": {\"name\": \"Adam\", \"weight_decay\": null, \"clipnorm\": null, \"global_clipnorm\": null, \"clipvalue\": null, \"use_ema\": false, \"ema_momentum\": 0.99, \"ema_overwrite_frequency\": null, \"jit_compile\": false, \"is_legacy_optimizer\": false, \"learning_rate\": 0.0010000000474974513, \"beta_1\": 0.9, \"beta_2\": 0.999, \"epsilon\": 1e-07, \"amsgrad\": false}}}"
group /model_weights
	attribute backend = "tensorflow"
	attribute keras_version = "2.11.0"
	attribute layer_names = "input_1", "conv2d", "max_pooling2d", "conv2d_1", "max_pooling2d_1"
group /model_weights/conv2d
	attribute weight_names = "conv2d/kernel:0", "conv2d/bias:0"
group /model_weights/conv2d/conv2d
dataset /model_weights/conv2d/conv2d/bias:0: float32, shape (32)
dataset /model_weights/conv2d/conv2d/kernel:0: float32, shape (3, 3, 3, 32)
group /model_weights/conv2d_1
	attribute weight_names = "conv2d_1/kernel:0", "conv2d_1/bias:0"
group /model_weights/conv2d_1/conv2d_1
dataset /model_weights/conv2d_1/conv2d_1/bias:0: float32, shape (16)
dataset /model_weights/conv2d_1/conv2d_1/kernel:0: float32, shape (3, 3, 32, 16)
group /model_weights/input_1
	attribute weight_names = (float64, not shown)
group /model_weights/max_pooling2d
	attribute weight_names = (float64, not shown)
group /model_weights/max_pooling2d_1
	attribute weight_names = (float64, not shown)
group /model_weights/top_level_model_weights
	attribute weight_names = (float64, not shown)
group /optimizer_weights
	attribute weight_names = "iteration:0"
dataset /optimizer_weights/iteration:0: int64, shape ()