Test

A1_convert_mnist.py

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+# Copyright 2022 Arnd Koeppe and the CIDS team
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+from pathlib import Path
+
+import tensorflow as tf
+import tqdm
+
+from cids.data import DataDefinition
+from cids.data import DataWriter
+from cids.data import Feature
+from kadi_ai import KadiAIProject
+
+
+################################################################################
+# Data paths
+
+project_name = "ex-mnist"
+project_dir = Path.cwd() / "DATA" / project_name
+project = KadiAIProject(project_name, root=project_dir)
+
+# Project creates an `input_dir` in the `project_dir`, which stores converted
+#   input data as tfrecords in a subdirectory `tfrecord`
+tfrecord_dir = Path(project.input_dir) / "tfrecord"
+
+
+################################################################################
+# Data definition
+
+data_definition = DataDefinition(
+    Feature(
+        "image",
+        [None, 28, 28, 1],
+        data_format="NXYF",
+        dtype=tf.string,
+        decode_str_to=tf.float32,
+    ),
+    Feature(
+        "label", [None, 1], data_format="NF", dtype=tf.string, decode_str_to=tf.float32
+    ),
+    dtype=tf.float32,
+)
+
+project.data_definition = data_definition
+
+
+################################################################################
+# Read data
+
+(train_images, train_labels), (
+    test_images,
+    test_labels,
+) = tf.keras.datasets.mnist.load_data()
+
+src_data = list(zip(train_images, train_labels))
+
+
+################################################################################
+# Data processing
+
+
+def read_and_process(src_sample):
+    """Read and process source data."""
+    # Do some preprocessing
+    image = src_sample[0]
+    image = (image - 127.5) / 127.5
+    # Pack into dictionary
+    sample = {}
+    sample["image"] = image
+    sample["label"] = src_sample[1]
+    return sample
+
+
+################################################################################
+# Start processing
+
+# Create a data converter object
+data_writer = DataWriter(data_definition)
+
+# Loop over all pairs of source files with a pretty progress bar
+n = 0
+for src_sample in tqdm.tqdm(
+    src_data,
+    total=len(src_data),
+    file=project.stream_to_logger(),
+    leave=True,
+    desc="Conversion",
+    unit="sources",
+    dynamic_ncols=True,
+):
+    # Process sample
+    sample = read_and_process(src_sample)
+    out_file = tfrecord_dir / f"sample{n:05d}.tfrecord"
+    # Write sample to file
+    try:
+        data_writer.write_example(out_file, sample)
+    except KeyError as e:
+        project.warn(f"Missing key {e.args[0]} in: {os.fspath(out_file)}")
+        continue
+    n += 1
+    project.log(f"Done processing: {os.fspath(out_file)}")
+
+# Write the data definition and the features to a human-readable json file
+#   The json file can also be loaded directly later-on for training.
+project.data_definition = data_definition
+project.to_json(write_data_definition=True)
+
+project.log("Done.")

A2_train_mnist_modified.py

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+# Copyright 2022 Arnd Koeppe and the CIDS team
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
+
+import cids
+
+import numpy as np
+import tensorflow as tf
+import seaborn as sns
+
+from pathlib import Path
+from tensorflow.keras import layers as klayers
+from cids.tensorflow import layers as clayers
+from cids.tensorflow.tuner import SearchResults
+from cids.statistics import metrics
+from kadi_ai import KadiAIProject
+from matplotlib import pyplot as plt
+from kerastuner import HyperParameters
+
+# Preamble
+plt.style.use("seaborn-v0_8-paper")  # seaborn-talk, seaborn-poster, seaborn-paper
+plt.rcParams.update(
+    {
+        "font.family": "sans-serif",
+        "figure.dpi": 300,
+        "savefig.format": "png",
+    }
+)
+
+
+################################################################################
+# Controls
+
+CHECK = True
+SEARCH = False
+USE_BEST_SEARCH_CONFIG = False
+TRAIN = True
+EVAL = True
+PLOT = True
+ANALYZE = False
+
+TRAIN_CONTINUE = False
+
+
+num_check_samples = 100
+num_plot_samples = 20
+num_principal_components = 3
+
+
+################################################################################
+# Data paths
+
+project_name = "ex-mnist"
+project_dir = Path.cwd() / "DATA" / project_name
+project = KadiAIProject(project_name, root=project_dir)
+
+# Read paths
+train_samples, valid_samples, test_samples = project.get_split_datasets(
+    shuffle=True, valid_split=0.15, test_split=0.15
+)
+
+
+################################################################################
+# Data definition
+
+data_definition = project.data_definition
+data_definition.input_features = ["image"]
+data_definition.output_features = ["label"]
+
+
+################################################################################
+# Neural network
+
+# Model
+def model_function(hp, data_definition):
+
+    # Hyper parameters
+    num_kernels = hp.Choice("num_kernels", [32, 64, 128, 256, 512], default=64)
+    dropout_rate = hp.Float("dropout", 0.0, 0.7, default=0.3)  # not used
+
+    # Ref: https://github.com/AyaanZaveri/mnist/blob/main/MNIST_Number.ipynb
+    layers = []
+    layers.append(klayers.Conv2D(num_kernels, (3, 3), strides=(1, 1), padding="same"))
+    layers.append(klayers.ReLU())
+    layers.append(klayers.Conv2D(num_kernels, (3, 3), strides=(1, 1), padding="same"))
+    layers.append(klayers.ReLU())
+    # layers.append(klayers.Dropout(dropout_rate))
+    layers.append(klayers.MaxPooling2D(pool_size=(2, 2)))
+    layers.append(klayers.BatchNormalization())
+    
+    layers.append(klayers.Conv2D(num_kernels*2, (3, 3), strides=(1, 1), padding="same"))
+    layers.append(klayers.ReLU())
+    layers.append(klayers.Conv2D(num_kernels*2, (3, 3), strides=(1, 1), padding="same"))
+    layers.append(klayers.ReLU())
+    
+    layers.append(klayers.MaxPooling2D(pool_size=(2, 2)))
+    layers.append(klayers.BatchNormalization())
+    
+    layers.append(klayers.Conv2D(num_kernels*4, (3, 3), strides=(1, 1), padding="same"))
+    layers.append(klayers.MaxPooling2D(pool_size=(2, 2)))
+    
+    layers.append(klayers.Flatten())
+    layers.append(klayers.Dropout(dropout_rate))
+    layers.append(klayers.Dense(512))
+    layers.append(klayers.Dense(10, activation="softmax"))
+    return tf.keras.Sequential(layers)
+
+
+# Set a model name
+model_name = "mnist"
+model_name += "--" + "--".join(
+    [
+        "+".join(list(data_definition.input_features)),
+        "+".join(list(data_definition.output_features)),
+    ]
+)
+model_name += "--onehot"
+
+
+# Training schedule
+def schedule_function(hp):
+    learning_rate = hp.Choice(
+        "learning_rate", [3e-3, 1e-3, 3e-4, 1e-4, 3e-5, 1e-5], default=1e-4
+    )
+    batch_size = 256  # divided by number of GPUs
+    schedule = {
+        "count": [1, 21],
+        "learning_rate": learning_rate,
+        "batch_size": batch_size,
+    }
+    return schedule
+
+
+model = cids.CIDSModel.categorical_classification(
+    10,
+    data_definition,
+    model_function,
+    name=model_name,
+    identifier="",
+    result_dir=project.result_dir,
+)
+model.encode_categorical = "outputs"
+model.metrics.append("accuracy")
+model.monitor = "val_accuracy"
+model.online_normalize = False
+model.data_reader.prefetch = "cache"
+
+
+if CHECK:
+    if PLOT:
+        check_samples = model.read_tfrecords(
+            train_samples[:num_check_samples], disable_feature_merge=True
+        )
+        project.log("Plotting feature distributions.")
+        model.plot_feature_distribution(check_samples, project.input_dir)
+
+
+if SEARCH:
+    project.log(">> Hyperparameters: searching...")
+    hp = model.search(
+        train_samples,
+        valid_samples,
+        schedule=schedule_function,
+        executions_per_trial=1,
+        max_epochs=3,  # hyperband only
+        overwrite=False,
+        objective="val_mae",
+        method="hyperband",
+        # method="bayes",
+        num_trials=3,
+        # callbacks=[tf.keras.callbacks.EarlyStopping(patience=5)],
+    )
+    if PLOT:
+        search_results = SearchResults(model)
+        search_results.plot_hyperparameter_search()
+    model.identifier = "best"
+    project.log(">> Hyperparameters: search complete.")
+elif USE_BEST_SEARCH_CONFIG:
+    try:
+        search_results = SearchResults(model)
+        hps = search_results.get_best_hyperparameters(print="best")
+        hp = hps[0]
+        model.identifier = "best"
+        project.log(">> Hyperparameters: loaded from previous search.")
+    except (FileNotFoundError, PermissionError) as e:
+        project.log(">> Hyperparameters: " + str(e))
+        project.log(">> Hyperparameters: No search found. Defaults loaded.")
+        hp = HyperParameters()
+        model.identifier = "default"
+else:
+    project.log(">> Hyperparameters: Defaults loaded.")
+    hp = HyperParameters()
+    model.identifier = "default"
+
+
+if TRAIN:
+    project.log(">> Training...")
+    if TRAIN_CONTINUE:
+        project.log(">> ...continuing from last...")
+        checkpoint = "last"
+    else:
+        project.log(">> ...starting new...")
+        checkpoint = None
+    model.VERBOSITY = 2
+    model.train(
+        train_samples,
+        valid_samples,
+        schedule=schedule_function,
+        checkpoint=checkpoint,
+        hp=hp,
+    )
+    # save hp
+    import json
+    hp_path = os.path.join(model.model_dir, "hp.json")
+    with open(hp_path, "w") as f:
+        json.dump(model._hp.get_config(), f)
+    print(model.model_dir)
+    print(model._hp)
+    print(model._hp.values)
+    print(model._checkpoint_dir)
+    print(model.model_dir)
+    exit()
+    project.log(">> Training complete.")
+
+if EVAL:
+    project.log(">> Evaluating...")
+    project.log(">>> Metrics...")
+    # Compute predictions
+    # test_loss = model.eval_data(
+    #     test_samples, batch_size=4, checkpoint="last", hp=hp, submodels="generator")
+    X, Y, Y_ = model.infer_data(
+        test_samples,
+        batch_size=4,
+        checkpoint="last",
+        hp=hp,
+    )
+    test_result_file = Path(model.base_model_dir, "test_results.npz")
+    np.savez(test_result_file, X=X, Y=Y, Y_=Y_)
+    # Evaluate metrics
+    total_metrics = {}
+    total_metrics["mae"] = metrics.mean_absolute_error(Y, Y_)
+    total_metrics["mape"] = metrics.mean_absolute_percentage_error(Y, Y_)
+    total_metrics["smape"] = metrics.symmetric_mean_absolute_percentage_error(Y, Y_)
+    total_metrics["rmse"] = metrics.root_mean_square_error(Y, Y_)
+    total_metrics["nrmse"] = metrics.normalized_root_mean_square_error(Y, Y_)
+    project.log("Total metrics")
+    for k, v in total_metrics.items():
+        project.log(f"   {k}: {v}")
+    project.log("")
+
+    # Feature metrics
+    feature_metrics = {}
+    feature_metrics["mae"] = metrics.mean_absolute_error(Y, Y_, reduction_axes=(0,))
+    feature_metrics["mape"] = metrics.mean_absolute_percentage_error(
+        Y, Y_, reduction_axes=(0,)
+    )
+    feature_metrics["smape"] = metrics.symmetric_mean_absolute_percentage_error(
+        Y, Y_, reduction_axes=(0,)
+    )
+    feature_metrics["rmse"] = metrics.root_mean_square_error(Y, Y_, reduction_axes=(0,))
+    feature_metrics["nrmse"] = metrics.normalized_root_mean_square_error(
+        Y, Y_, reduction_axes=(0,)
+    )
+    project.log("Feature metrics")
+    for k, v in feature_metrics.items():
+        project.log(f"   {k}: {v}")
+    project.log("")
+
+    # Sample metrics
+    sample_metrics = {}
+    sample_metrics["mae"] = metrics.mean_absolute_error(Y, Y_, reduction_axes=(1,))
+    sample_metrics["mape"] = metrics.mean_absolute_percentage_error(
+        Y, Y_, reduction_axes=(1,)
+    )
+    sample_metrics["smape"] = metrics.symmetric_mean_absolute_percentage_error(
+        Y, Y_, reduction_axes=(1,)
+    )
+    sample_metrics["rmse"] = metrics.root_mean_square_error(Y, Y_, reduction_axes=(1,))
+    sample_metrics["nrmse"] = metrics.normalized_root_mean_square_error(
+        Y, Y_, reduction_axes=(1,)
+    )
+    project.log("Sample metrics:")
+    for k, v in sample_metrics.items():
+        project.log(f"   {k}: {v}")
+    project.log("")
+
+    # Select worst, best, mean, median samples
+    error_metric = "mae"
+    sample_errors = sample_metrics[error_metric]
+    total_error = total_metrics[error_metric]
+    sorted_sample_ids = np.argsort(sample_errors)
+    sorted_sample_errors = sample_errors[sorted_sample_ids]
+    # nonconvergence_cutoff = 100000.0
+    # converged_sorted_sample_errors = sorted_sample_errors[
+    #     sorted_sample_errors < nonconvergence_cutoff
+    # ]
+    # converged_sorted_sample_ids = sorted_sample_ids[
+    #     sorted_sample_errors < nonconvergence_cutoff
+    # ]
+    converged_sorted_sample_ids = sorted_sample_ids
+    converged_sorted_sample_errors = sorted_sample_errors
+    best_sample_id = converged_sorted_sample_ids[0]
+    worst_sample_id = converged_sorted_sample_ids[-1]
+    median_sample_id = converged_sorted_sample_ids[
+        len(converged_sorted_sample_ids) // 2
+    ]
+    perc25_sample_id = converged_sorted_sample_ids[
+        len(converged_sorted_sample_ids) * 1 // 4
+    ]
+    perc75_sample_id = converged_sorted_sample_ids[
+        len(converged_sorted_sample_ids) * 3 // 4
+    ]
+    mean_sample_id = np.argmin(
+        np.abs(sample_errors - np.mean(converged_sorted_sample_errors))
+    )
+    project.log(f"{project_name}: Sample quality: sample {error_metric}")
+    project.log(
+        f"{project_name}:\n"
+        + f"    mean: {sample_errors[mean_sample_id]}"
+        + f" ({test_samples[mean_sample_id]})\n"
+        + f"    best: {sample_errors[best_sample_id]}"
+        + f" ({test_samples[best_sample_id]})\n"
+        + f"    worst: {sample_errors[worst_sample_id]}"
+        + f" ({test_samples[worst_sample_id]})\n"
+        + f"    median: {sample_errors[median_sample_id]}"
+        + f" ({test_samples[median_sample_id]})\n"
+        + f"    perc25: {sample_errors[perc25_sample_id]}"
+        + f" ({test_samples[perc25_sample_id]})\n"
+        + f"    perc75: {sample_errors[perc75_sample_id]}"
+        + f" ({test_samples[perc75_sample_id]})\n"
+    )
+
+# Plot
+if PLOT:
+    project.log(">> Plotting results...")
+    test_result_file = Path(model.base_model_dir, "test_results.npz")
+    test_results = np.load(test_result_file)
+    X = test_results["X"][:num_plot_samples]
+    Y = test_results["Y"][:num_plot_samples]
+    Y_ = test_results["Y_"][:num_plot_samples]
+    # Plot individual samples
+    project.log(">>> Plotting individual samples")
+    for i, (x, y, y_) in enumerate(zip(X, Y, Y_)):
+        image = np.squeeze(x)
+        label = np.argmax(y, axis=-1)
+        prediction = np.argmax(y_, axis=-1)
+        # Draw
+        fig = plt.figure(figsize=(4, 3))
+        plt.imshow(image, cmap=sns.color_palette("mako_r", as_cmap=True))
+        plt.axis("off")
+        fig.suptitle(
+            f"label = {label:d}, prediction = {prediction:d} ({y_[prediction]})"
+        )
+        fig.tight_layout()
+        # Save and close
+        plot_file = os.path.join(
+            model.plot_dir, f"best_to_worst_{i:05d}_label={label:d}"
+        )
+        plt.savefig(plot_file)
+        plt.close()
+
+# Explain
+if ANALYZE:
+    analze_samples = test_samples[:10]
+    X, Y = model.read_tfrecords(test_samples[:num_plot_samples])
+    # # PCA analysis
+    # Y_, C1, C2 = model.predict(X, return_states=True)
+    # for si in range(num_plot_samples):
+    #     # Compute PCA on single sample      # TODO: pca over multiple samples?
+    #     cov = np.dot(C1[si, ...].T, C1[si, ...])
+    #     u, s, v = np.linalg.svd(cov, compute_uv=True)
+    #     pc_axes = np.dot(C1[si, ...], u[:, :num_principal_components])
+    #     # Plot loading and relaxation
+    #     fig, axes = plt.subplots(3, 1, sharex=True, figsize=(7, 6),
+    #                              gridspec_kw={"height_ratios": [0.5, 1, 1]})
+    #     plt.sca(axes[0])
+    #     plt.plot(X[si, :, 0], "C0", label="strain")
+    #     plt.ylabel("strain [-]")
+    #     plt.sca(axes[1])
+    #     plt.plot(Y[si, :, 1], "C2", label="strain_plastic_ref")
+    #     plt.plot(Y_[si, :, 1], "C2", linestyle="dashed",
+    #              label="strain_plastic")
+    #     for pc in range(num_principal_components):
+    #         plt.plot(pc_axes[:, pc], "C{:d}".format(3 + pc),
+    #                  linestyle="dotted", label="state{:d}".format(pc))
+    #     plt.ylabel("history [-]")
+    #     plt.sca(axes[2])
+    #     plt.plot(Y[si, :, 0], "C1", label="stress_ref")
+    #     plt.plot(Y_[si, :, 0], "C1", linestyle="dashed", label="stress")
+    #     plt.ylabel("stress [-]")
+    #     plt.xlabel("increments [-]")
+    #     fig.legend(loc="upper center", ncol=4)
+    #     plt.tight_layout()
+    #     plt.draw()
+    #     plt.savefig(os.path.join(model.plot_dir, "pca_{:06d}".format(si)))
+    #     plt.close()
+    # Local response propagation
+    analysis = model.analyze(
+        X[:num_plot_samples, ...],
+        method="lrp.epsilon",
+        neuron_selection_mode="all",
+        # neuron_selection=0,
+        plot=True,
+        checkpoint="last",
+    )
+    project.log(">> Evaluation complete.")
+
+
+################################################################################
+# Finished
+
+project.log("done")

DATA/ex-mnist/LOGS/2024-07-23_15-32-16_A2_train_mnist_modified.log

@@ -0,0 +1,157 @@
+[2024-07-23 15:32:16,573)](INFO)KadiAIProject: Logging to /mnt/data2/yinghanz/codes/frameworks/test/cids/DATA/ex-mnist/LOGS/2024-07-23_15-32-16_A2_train_mnist_mine.log
+[2024-07-23 15:32:16,575)](INFO)KadiAIProject: Setting seeds to: ex-mnist
+[2024-07-23 15:32:16,578)](INFO)KadiAIProject: Loading from /mnt/data2/yinghanz/codes/frameworks/test/cids/DATA/ex-mnist/project_ex-mnist.json
+[2024-07-23 15:32:19,552)](INFO)KadiAIProject: Git commit: 143d0296
+[2024-07-23 15:32:33,830)](INFO)KadiAIProject: Reusing dataset split from previous run. Change project name or set enforce_new_split=True for new split.
+[2024-07-23 15:32:38,183)](INFO)CIDSModelTF: Single CPU found. Using one device distribution.
+[2024-07-23 15:32:38,185)](INFO)DataReader: (test) Estimated sample size: 0.00309086 MB.
+[2024-07-23 15:32:38,186)](INFO)DataReader: (test) buffer_size set to maximum number of samples (100).
+[2024-07-23 15:32:38,804)](INFO)KadiAIProject: Plotting feature distributions.
+[2024-07-23 15:32:40,004)](INFO)KadiAIProject: >> Hyperparameters: Defaults loaded.
+[2024-07-23 15:32:40,005)](INFO)KadiAIProject: >> Training...
+[2024-07-23 15:32:40,006)](INFO)KadiAIProject: >> ...starting new...
+[2024-07-23 15:32:40,007)](INFO)CIDSModelTF: Starting training schedule.
+[2024-07-23 15:32:40,008)](INFO)CIDSModelTF: Starting training phase 0.
+[2024-07-23 15:32:40,161)](INFO)CIDSModelTF: No checkpoint loaded.
+[2024-07-23 15:32:40,196)](WARNING)CIDSModelTF: Clearing non-empty model directory.
+[2024-07-23 15:32:40,199)](INFO)CIDSModelTF: Model Summary: mnist--image--label--onehot-default-64C3-64C3-MP2-128C3-128C3-MP2-256C3-MP2-512-10
+[2024-07-23 15:32:40,200)](INFO)CIDSModelTF: Model: "InputPreprocess"
+[2024-07-23 15:32:40,201)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 15:32:40,201)](INFO)CIDSModelTF:  Layer (type)                Output Shape              Param #   
+[2024-07-23 15:32:40,202)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 15:32:40,202)](INFO)CIDSModelTF:  input_1 (InputLayer)        [(None, 28, 28, 1)]       0         
+[2024-07-23 15:32:40,203)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,203)](INFO)CIDSModelTF:  tf.identity (TFOpLambda)    (None, 28, 28, 1)         0         
+[2024-07-23 15:32:40,204)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,204)](INFO)CIDSModelTF:  tf.cast (TFOpLambda)        (None, 28, 28, 1)         0         
+[2024-07-23 15:32:40,205)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,205)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 15:32:40,205)](INFO)CIDSModelTF: Total params: 0
+[2024-07-23 15:32:40,206)](INFO)CIDSModelTF: Trainable params: 0
+[2024-07-23 15:32:40,206)](INFO)CIDSModelTF: Non-trainable params: 0
+[2024-07-23 15:32:40,207)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 15:32:40,207)](INFO)CIDSModelTF: Model: "OutputPreprocess"
+[2024-07-23 15:32:40,208)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 15:32:40,208)](INFO)CIDSModelTF:  Layer (type)                Output Shape              Param #   
+[2024-07-23 15:32:40,209)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 15:32:40,209)](INFO)CIDSModelTF:  input_2 (InputLayer)        [(None, 1)]               0         
+[2024-07-23 15:32:40,210)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,214)](INFO)CIDSModelTF:  tf.identity_1 (TFOpLambda)  (None, 1)                 0         
+[2024-07-23 15:32:40,214)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,215)](INFO)CIDSModelTF:  tf.__operators__.getitem (S  (None,)                  0         
+[2024-07-23 15:32:40,215)](INFO)CIDSModelTF:  licingOpLambda)                                                 
+[2024-07-23 15:32:40,216)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,216)](INFO)CIDSModelTF:  tf.cast_1 (TFOpLambda)      (None,)                   0         
+[2024-07-23 15:32:40,216)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,217)](INFO)CIDSModelTF:  tf.one_hot (TFOpLambda)     (None, 10)                0         
+[2024-07-23 15:32:40,217)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,218)](INFO)CIDSModelTF:  tf.cast_2 (TFOpLambda)      (None, 10)                0         
+[2024-07-23 15:32:40,218)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,219)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 15:32:40,219)](INFO)CIDSModelTF: Total params: 0
+[2024-07-23 15:32:40,219)](INFO)CIDSModelTF: Trainable params: 0
+[2024-07-23 15:32:40,220)](INFO)CIDSModelTF: Non-trainable params: 0
+[2024-07-23 15:32:40,220)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 15:32:40,221)](INFO)CIDSModelTF: Model: "Core"
+[2024-07-23 15:32:40,221)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 15:32:40,221)](INFO)CIDSModelTF:  Layer (type)                Output Shape              Param #   
+[2024-07-23 15:32:40,222)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 15:32:40,222)](INFO)CIDSModelTF:  input_3 (InputLayer)        [(None, 28, 28, 1)]       0         
+[2024-07-23 15:32:40,223)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,223)](INFO)CIDSModelTF:  conv2d (Conv2D)             (None, 28, 28, 64)        640       
+[2024-07-23 15:32:40,224)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,224)](INFO)CIDSModelTF:  re_lu (ReLU)                (None, 28, 28, 64)        0         
+[2024-07-23 15:32:40,225)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,225)](INFO)CIDSModelTF:  conv2d_1 (Conv2D)           (None, 28, 28, 64)        36928     
+[2024-07-23 15:32:40,225)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,226)](INFO)CIDSModelTF:  re_lu_1 (ReLU)              (None, 28, 28, 64)        0         
+[2024-07-23 15:32:40,226)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,227)](INFO)CIDSModelTF:  max_pooling2d (MaxPooling2D  (None, 14, 14, 64)       0         
+[2024-07-23 15:32:40,227)](INFO)CIDSModelTF:  )                                                               
+[2024-07-23 15:32:40,228)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,228)](INFO)CIDSModelTF:  batch_normalization (BatchN  (None, 14, 14, 64)       256       
+[2024-07-23 15:32:40,228)](INFO)CIDSModelTF:  ormalization)                                                   
+[2024-07-23 15:32:40,229)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,229)](INFO)CIDSModelTF:  conv2d_2 (Conv2D)           (None, 14, 14, 128)       73856     
+[2024-07-23 15:32:40,230)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,230)](INFO)CIDSModelTF:  re_lu_2 (ReLU)              (None, 14, 14, 128)       0         
+[2024-07-23 15:32:40,231)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,231)](INFO)CIDSModelTF:  conv2d_3 (Conv2D)           (None, 14, 14, 128)       147584    
+[2024-07-23 15:32:40,232)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,232)](INFO)CIDSModelTF:  re_lu_3 (ReLU)              (None, 14, 14, 128)       0         
+[2024-07-23 15:32:40,233)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,233)](INFO)CIDSModelTF:  max_pooling2d_1 (MaxPooling  (None, 7, 7, 128)        0         
+[2024-07-23 15:32:40,234)](INFO)CIDSModelTF:  2D)                                                             
+[2024-07-23 15:32:40,234)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,235)](INFO)CIDSModelTF:  batch_normalization_1 (Batc  (None, 7, 7, 128)        512       
+[2024-07-23 15:32:40,235)](INFO)CIDSModelTF:  hNormalization)                                                 
+[2024-07-23 15:32:40,236)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,236)](INFO)CIDSModelTF:  conv2d_4 (Conv2D)           (None, 7, 7, 256)         295168    
+[2024-07-23 15:32:40,236)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,237)](INFO)CIDSModelTF:  max_pooling2d_2 (MaxPooling  (None, 3, 3, 256)        0         
+[2024-07-23 15:32:40,237)](INFO)CIDSModelTF:  2D)                                                             
+[2024-07-23 15:32:40,238)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,238)](INFO)CIDSModelTF:  flatten (Flatten)           (None, 2304)              0         
+[2024-07-23 15:32:40,239)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,239)](INFO)CIDSModelTF:  dropout (Dropout)           (None, 2304)              0         
+[2024-07-23 15:32:40,240)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,240)](INFO)CIDSModelTF:  dense (Dense)               (None, 512)               1180160   
+[2024-07-23 15:32:40,241)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,242)](INFO)CIDSModelTF:  dense_1 (Dense)             (None, 10)                5130      
+[2024-07-23 15:32:40,242)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,243)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 15:32:40,244)](INFO)CIDSModelTF: Total params: 1,740,234
+[2024-07-23 15:32:40,244)](INFO)CIDSModelTF: Trainable params: 1,739,850
+[2024-07-23 15:32:40,245)](INFO)CIDSModelTF: Non-trainable params: 384
+[2024-07-23 15:32:40,245)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 15:32:40,246)](INFO)CIDSModelTF: Model: "Postprocess"
+[2024-07-23 15:32:40,246)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 15:32:40,247)](INFO)CIDSModelTF:  Layer (type)                Output Shape              Param #   
+[2024-07-23 15:32:40,247)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 15:32:40,248)](INFO)CIDSModelTF:  input_5 (InputLayer)        [(None, 10)]              0         
+[2024-07-23 15:32:40,248)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,249)](INFO)CIDSModelTF:  tf.math.argmax (TFOpLambda)  (None,)                  0         
+[2024-07-23 15:32:40,250)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,250)](INFO)CIDSModelTF:  tf.expand_dims (TFOpLambda)  (None, 1)                0         
+[2024-07-23 15:32:40,251)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 15:32:40,252)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 15:32:40,252)](INFO)CIDSModelTF: Total params: 0
+[2024-07-23 15:32:40,253)](INFO)CIDSModelTF: Trainable params: 0
+[2024-07-23 15:32:40,254)](INFO)CIDSModelTF: Non-trainable params: 0
+[2024-07-23 15:32:40,254)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 15:32:40,256)](INFO)CIDSModelTF: Estimated memory (Core model): 0.349000 GBytes
+[2024-07-23 15:32:40,257)](INFO)CIDSModelTF: Estimated memory (Input preprocess model): 0.002000 GBytes
+[2024-07-23 15:32:40,258)](INFO)CIDSModelTF: Estimated memory (Output preprocess model): 0.000000 GBytes
+[2024-07-23 15:32:40,258)](INFO)CIDSModelTF: Estimated memory (Postprocess model): 0.000000 GBytes
+[2024-07-23 15:32:40,259)](INFO)CIDSModelTF: Plotting models.
+[2024-07-23 15:32:40,465)](INFO)DataReader: (train) Caching dataset. Disable by setting model.data_reader.prefetch = True. Currently set to: "cache".
+[2024-07-23 15:32:41,136)](INFO)DataReader: (valid) Caching dataset. Disable by setting model.data_reader.prefetch = True. Currently set to: "cache".
+[2024-07-23 15:32:41,388)](INFO)CIDSModelTF: Training phase 0:   0%|          | 0/1 [00:00<?, ?it/s]
+[2024-07-23 15:34:12,048)](INFO)CIDSModelTF: Training phase 0: 100%|##########| 1/1 [01:30<00:00, 90.66s/it, epoch=0001, accuracy=0.902, loss=0.317, val_accuracy=0.116, val_loss=3.11]
+[2024-07-23 15:34:12,355)](INFO)CIDSModelTF: Training phase 0: 100%|##########| 1/1 [01:30<00:00, 90.97s/it, epoch=0001, accuracy=0.902, loss=0.317, val_accuracy=0.116, val_loss=3.11]
+[2024-07-23 15:34:12,380)](INFO)CIDSModelTF: Training phase finished successfully.
+[2024-07-23 15:34:12,452)](INFO)CIDSModelTF: Starting training phase 1.
+[2024-07-23 15:34:12,526)](INFO)CIDSModelTF: Training phase 1:   0%|          | 0/20 [00:00<?, ?it/s]
+[2024-07-23 15:34:46,689)](INFO)CIDSModelTF: Training phase 1:   5%|5         | 1/20 [00:34<10:49, 34.16s/it, epoch=0002, accuracy=0.978, loss=0.0697, val_accuracy=0.147, val_loss=3.06]
+[2024-07-23 15:35:21,365)](INFO)CIDSModelTF: Training phase 1:  10%|#         | 2/20 [01:08<10:20, 34.46s/it, epoch=0003, accuracy=0.984, loss=0.0504, val_accuracy=0.857, val_loss=0.4]
+[2024-07-23 15:35:55,239)](INFO)CIDSModelTF: Training phase 1:  15%|#5        | 3/20 [01:42<09:41, 34.19s/it, epoch=0004, accuracy=0.991, loss=0.0301, val_accuracy=0.982, val_loss=0.0621]
+[2024-07-23 15:36:29,156)](INFO)CIDSModelTF: Training phase 1:  20%|##        | 4/20 [02:16<09:05, 34.08s/it, epoch=0005, accuracy=0.992, loss=0.0236, val_accuracy=0.987, val_loss=0.0386]
+[2024-07-23 15:37:02,554)](INFO)CIDSModelTF: Training phase 1:  25%|##5       | 5/20 [02:50<08:27, 33.84s/it, epoch=0006, accuracy=0.996, loss=0.0154, val_accuracy=0.987, val_loss=0.0368]
+[2024-07-23 15:37:35,633)](INFO)CIDSModelTF: Training phase 1:  30%|###       | 6/20 [03:23<07:50, 33.58s/it, epoch=0007, accuracy=0.997, loss=0.0115, val_accuracy=0.988, val_loss=0.0381]
+[2024-07-23 15:38:08,646)](INFO)CIDSModelTF: Training phase 1:  35%|###5      | 7/20 [03:56<07:14, 33.39s/it, epoch=0008, accuracy=0.998, loss=0.0086, val_accuracy=0.99, val_loss=0.0331]
+[2024-07-23 15:38:42,378)](INFO)CIDSModelTF: Training phase 1:  40%|####      | 8/20 [04:29<06:42, 33.50s/it, epoch=0009, accuracy=0.998, loss=0.00658, val_accuracy=0.99, val_loss=0.0356]
+[2024-07-23 15:39:17,075)](INFO)CIDSModelTF: Training phase 1:  45%|####5     | 9/20 [05:04<06:12, 33.88s/it, epoch=0010, accuracy=0.998, loss=0.00761, val_accuracy=0.99, val_loss=0.0328]
+[2024-07-23 15:39:51,655)](INFO)CIDSModelTF: Training phase 1:  50%|#####     | 10/20 [05:39<05:40, 34.09s/it, epoch=0011, accuracy=0.998, loss=0.00537, val_accuracy=0.99, val_loss=0.034]
+[2024-07-23 15:40:26,613)](INFO)CIDSModelTF: Training phase 1:  55%|#####5    | 11/20 [06:14<05:09, 34.36s/it, epoch=0012, accuracy=0.999, loss=0.00374, val_accuracy=0.99, val_loss=0.0339]
+[2024-07-23 15:41:01,613)](INFO)CIDSModelTF: Training phase 1:  60%|######    | 12/20 [06:49<04:36, 34.55s/it, epoch=0013, accuracy=0.999, loss=0.00393, val_accuracy=0.989, val_loss=0.0366]
+[2024-07-23 15:41:36,608)](INFO)CIDSModelTF: Training phase 1:  65%|######5   | 13/20 [07:24<04:02, 34.69s/it, epoch=0014, accuracy=0.999, loss=0.00374, val_accuracy=0.988, val_loss=0.039]
+[2024-07-23 15:42:11,805)](INFO)CIDSModelTF: Training phase 1:  70%|#######   | 14/20 [07:59<03:29, 34.84s/it, epoch=0015, accuracy=0.999, loss=0.00329, val_accuracy=0.988, val_loss=0.0427]
+[2024-07-23 15:42:47,331)](INFO)CIDSModelTF: Training phase 1:  75%|#######5  | 15/20 [08:34<02:55, 35.05s/it, epoch=0016, accuracy=0.998, loss=0.0047, val_accuracy=0.992, val_loss=0.0312]
+[2024-07-23 15:43:22,394)](INFO)CIDSModelTF: Training phase 1:  80%|########  | 16/20 [09:09<02:20, 35.05s/it, epoch=0017, accuracy=0.999, loss=0.00354, val_accuracy=0.99, val_loss=0.0359]
+[2024-07-23 15:43:57,448)](INFO)CIDSModelTF: Training phase 1:  85%|########5 | 17/20 [09:44<01:45, 35.05s/it, epoch=0018, accuracy=0.999, loss=0.00273, val_accuracy=0.99, val_loss=0.0379]
+[2024-07-23 15:44:32,451)](INFO)CIDSModelTF: Training phase 1:  90%|######### | 18/20 [10:19<01:10, 35.04s/it, epoch=0019, accuracy=0.999, loss=0.00416, val_accuracy=0.99, val_loss=0.0399]
+[2024-07-23 15:45:05,360)](INFO)CIDSModelTF: Training phase 1:  95%|#########5| 19/20 [10:52<00:34, 34.40s/it, epoch=0020, accuracy=0.998, loss=0.00527, val_accuracy=0.99, val_loss=0.0445]
+[2024-07-23 15:45:40,337)](INFO)CIDSModelTF: Training phase 1: 100%|##########| 20/20 [11:27<00:00, 34.57s/it, epoch=0021, accuracy=0.998, loss=0.00522, val_accuracy=0.989, val_loss=0.041]
+[2024-07-23 15:45:40,551)](INFO)CIDSModelTF: Training phase 1: 100%|##########| 20/20 [11:28<00:00, 34.40s/it, epoch=0021, accuracy=0.998, loss=0.00522, val_accuracy=0.989, val_loss=0.041]
+[2024-07-23 15:45:40,588)](INFO)CIDSModelTF: Training phase finished successfully.
+[2024-07-23 15:45:40,675)](INFO)CIDSModelTF: Training schedule finished.

DATA/ex-mnist/LOGS/2024-07-23_16-08-52_app.log

@@ -0,0 +1,131 @@
+[2024-07-23 16:08:52,656)](INFO)KadiAIProject: Logging to /mnt/data2/yinghanz/codes/frameworks/test/cids_gr_example/DATA/ex-mnist/LOGS/2024-07-23_16-08-52_app.log
+[2024-07-23 16:08:52,658)](INFO)KadiAIProject: Setting seeds to: ex-mnist
+[2024-07-23 16:08:52,661)](INFO)KadiAIProject: Loading from /mnt/data2/yinghanz/codes/frameworks/test/cids_gr_example/DATA/ex-mnist/project_ex-mnist.json
+[2024-07-23 16:08:54,685)](INFO)KadiAIProject: Git commit: 143d0296
+[2024-07-23 16:08:54,894)](INFO)CIDSModelTF: Single CPU found. Using one device distribution.
+[2024-07-23 16:08:54,898)](INFO)KadiAIProject: >> Evaluating...
+[2024-07-23 16:08:54,899)](INFO)KadiAIProject: >>> Metrics...
+[2024-07-23 16:08:55,081)](WARNING)CIDSModelTF: Attempting to load checkpoint='last' but model.save_best_only = True. This will not load the final model state at the end of training but the last checkpoint that improved the model.monitor = 'val_accuracy'. Use checkpoint='last_phase' to get final state.
+[2024-07-23 16:08:55,085)](INFO)CIDSModelTF: Continuing from epoch 16 (checkpoint 0000016).
+[2024-07-23 16:08:55,186)](INFO)CIDSModelTF: Model Summary: mnist--image--label--onehot-default-64C3-64C3-MP2-128C3-128C3-MP2-256C3-MP2-512-10
+[2024-07-23 16:08:55,187)](INFO)CIDSModelTF: Model: "InputPreprocess"
+[2024-07-23 16:08:55,188)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:08:55,189)](INFO)CIDSModelTF:  Layer (type)                Output Shape              Param #   
+[2024-07-23 16:08:55,189)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:08:55,190)](INFO)CIDSModelTF:  input_1 (InputLayer)        [(None, 28, 28, 1)]       0         
+[2024-07-23 16:08:55,191)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,192)](INFO)CIDSModelTF:  tf.identity (TFOpLambda)    (None, 28, 28, 1)         0         
+[2024-07-23 16:08:55,193)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,193)](INFO)CIDSModelTF:  tf.cast (TFOpLambda)        (None, 28, 28, 1)         0         
+[2024-07-23 16:08:55,194)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,195)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:08:55,195)](INFO)CIDSModelTF: Total params: 0
+[2024-07-23 16:08:55,196)](INFO)CIDSModelTF: Trainable params: 0
+[2024-07-23 16:08:55,197)](INFO)CIDSModelTF: Non-trainable params: 0
+[2024-07-23 16:08:55,197)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:08:55,198)](INFO)CIDSModelTF: Model: "OutputPreprocess"
+[2024-07-23 16:08:55,198)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:08:55,199)](INFO)CIDSModelTF:  Layer (type)                Output Shape              Param #   
+[2024-07-23 16:08:55,200)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:08:55,200)](INFO)CIDSModelTF:  input_2 (InputLayer)        [(None, 1)]               0         
+[2024-07-23 16:08:55,201)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,201)](INFO)CIDSModelTF:  tf.identity_1 (TFOpLambda)  (None, 1)                 0         
+[2024-07-23 16:08:55,202)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,203)](INFO)CIDSModelTF:  tf.__operators__.getitem (S  (None,)                  0         
+[2024-07-23 16:08:55,203)](INFO)CIDSModelTF:  licingOpLambda)                                                 
+[2024-07-23 16:08:55,204)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,205)](INFO)CIDSModelTF:  tf.cast_1 (TFOpLambda)      (None,)                   0         
+[2024-07-23 16:08:55,205)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,206)](INFO)CIDSModelTF:  tf.one_hot (TFOpLambda)     (None, 10)                0         
+[2024-07-23 16:08:55,207)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,207)](INFO)CIDSModelTF:  tf.cast_2 (TFOpLambda)      (None, 10)                0         
+[2024-07-23 16:08:55,208)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,209)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:08:55,210)](INFO)CIDSModelTF: Total params: 0
+[2024-07-23 16:08:55,210)](INFO)CIDSModelTF: Trainable params: 0
+[2024-07-23 16:08:55,211)](INFO)CIDSModelTF: Non-trainable params: 0
+[2024-07-23 16:08:55,212)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:08:55,213)](INFO)CIDSModelTF: Model: "Core"
+[2024-07-23 16:08:55,213)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:08:55,214)](INFO)CIDSModelTF:  Layer (type)                Output Shape              Param #   
+[2024-07-23 16:08:55,215)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:08:55,216)](INFO)CIDSModelTF:  input_3 (InputLayer)        [(None, 28, 28, 1)]       0         
+[2024-07-23 16:08:55,217)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,217)](INFO)CIDSModelTF:  conv2d (Conv2D)             (None, 28, 28, 64)        640       
+[2024-07-23 16:08:55,218)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,219)](INFO)CIDSModelTF:  re_lu (ReLU)                (None, 28, 28, 64)        0         
+[2024-07-23 16:08:55,219)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,220)](INFO)CIDSModelTF:  conv2d_1 (Conv2D)           (None, 28, 28, 64)        36928     
+[2024-07-23 16:08:55,221)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,222)](INFO)CIDSModelTF:  re_lu_1 (ReLU)              (None, 28, 28, 64)        0         
+[2024-07-23 16:08:55,222)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,223)](INFO)CIDSModelTF:  max_pooling2d (MaxPooling2D  (None, 14, 14, 64)       0         
+[2024-07-23 16:08:55,223)](INFO)CIDSModelTF:  )                                                               
+[2024-07-23 16:08:55,224)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,225)](INFO)CIDSModelTF:  batch_normalization (BatchN  (None, 14, 14, 64)       256       
+[2024-07-23 16:08:55,225)](INFO)CIDSModelTF:  ormalization)                                                   
+[2024-07-23 16:08:55,226)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,227)](INFO)CIDSModelTF:  conv2d_2 (Conv2D)           (None, 14, 14, 128)       73856     
+[2024-07-23 16:08:55,227)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,228)](INFO)CIDSModelTF:  re_lu_2 (ReLU)              (None, 14, 14, 128)       0         
+[2024-07-23 16:08:55,229)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,230)](INFO)CIDSModelTF:  conv2d_3 (Conv2D)           (None, 14, 14, 128)       147584    
+[2024-07-23 16:08:55,230)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,231)](INFO)CIDSModelTF:  re_lu_3 (ReLU)              (None, 14, 14, 128)       0         
+[2024-07-23 16:08:55,231)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,232)](INFO)CIDSModelTF:  max_pooling2d_1 (MaxPooling  (None, 7, 7, 128)        0         
+[2024-07-23 16:08:55,232)](INFO)CIDSModelTF:  2D)                                                             
+[2024-07-23 16:08:55,233)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,234)](INFO)CIDSModelTF:  batch_normalization_1 (Batc  (None, 7, 7, 128)        512       
+[2024-07-23 16:08:55,234)](INFO)CIDSModelTF:  hNormalization)                                                 
+[2024-07-23 16:08:55,235)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,236)](INFO)CIDSModelTF:  conv2d_4 (Conv2D)           (None, 7, 7, 256)         295168    
+[2024-07-23 16:08:55,236)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,237)](INFO)CIDSModelTF:  max_pooling2d_2 (MaxPooling  (None, 3, 3, 256)        0         
+[2024-07-23 16:08:55,237)](INFO)CIDSModelTF:  2D)                                                             
+[2024-07-23 16:08:55,238)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,239)](INFO)CIDSModelTF:  flatten (Flatten)           (None, 2304)              0         
+[2024-07-23 16:08:55,239)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,240)](INFO)CIDSModelTF:  dropout (Dropout)           (None, 2304)              0         
+[2024-07-23 16:08:55,240)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,241)](INFO)CIDSModelTF:  dense (Dense)               (None, 512)               1180160   
+[2024-07-23 16:08:55,242)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,242)](INFO)CIDSModelTF:  dense_1 (Dense)             (None, 10)                5130      
+[2024-07-23 16:08:55,243)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,243)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:08:55,245)](INFO)CIDSModelTF: Total params: 1,740,234
+[2024-07-23 16:08:55,245)](INFO)CIDSModelTF: Trainable params: 1,739,850
+[2024-07-23 16:08:55,246)](INFO)CIDSModelTF: Non-trainable params: 384
+[2024-07-23 16:08:55,247)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:08:55,247)](INFO)CIDSModelTF: Model: "Postprocess"
+[2024-07-23 16:08:55,248)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:08:55,249)](INFO)CIDSModelTF:  Layer (type)                Output Shape              Param #   
+[2024-07-23 16:08:55,249)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:08:55,250)](INFO)CIDSModelTF:  input_5 (InputLayer)        [(None, 10)]              0         
+[2024-07-23 16:08:55,251)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,252)](INFO)CIDSModelTF:  tf.math.argmax (TFOpLambda)  (None,)                  0         
+[2024-07-23 16:08:55,252)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,253)](INFO)CIDSModelTF:  tf.expand_dims (TFOpLambda)  (None, 1)                0         
+[2024-07-23 16:08:55,254)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:08:55,254)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:08:55,255)](INFO)CIDSModelTF: Total params: 0
+[2024-07-23 16:08:55,255)](INFO)CIDSModelTF: Trainable params: 0
+[2024-07-23 16:08:55,256)](INFO)CIDSModelTF: Non-trainable params: 0
+[2024-07-23 16:08:55,256)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:08:55,259)](INFO)CIDSModelTF: Estimated memory (Core model): 0.012000 GBytes
+[2024-07-23 16:08:55,260)](INFO)CIDSModelTF: Estimated memory (Input preprocess model): 0.000000 GBytes
+[2024-07-23 16:08:55,260)](INFO)CIDSModelTF: Estimated memory (Output preprocess model): 0.000000 GBytes
+[2024-07-23 16:08:55,261)](INFO)CIDSModelTF: Estimated memory (Postprocess model): 0.000000 GBytes
+[2024-07-23 16:08:55,263)](INFO)DataReader: (test) Estimated sample size: 0.00309086 MB.
+[2024-07-23 16:08:55,264)](INFO)DataReader: (test) buffer_size set to maximum number of samples (1).
+[2024-07-23 16:08:55,680)](INFO)DataReader: (test) Caching dataset. Disable by setting model.data_reader.prefetch = True. Currently set to: "cache".
+[2024-07-23 16:08:55,684)](WARNING)DataReader: Batch size is larger than dataset. Repeating dataset and dropping remainder to achieve requested batch size.
+[2024-07-23 16:08:56,169)](INFO)CIDSModelTF: Preprocessing...
+[2024-07-23 16:08:56,453)](INFO)CIDSModelTF: Inferring...
+[2024-07-23 16:09:00,119)](INFO)CIDSModelTF: Postprocessing...
+[2024-07-23 16:09:00,384)](INFO)CIDSModelTF: Inference completed.
+[2024-07-23 16:09:00,592)](INFO)httpx: HTTP Request: GET https://checkip.amazonaws.com/ "HTTP/1.1 200 "
+[2024-07-23 16:09:00,821)](INFO)httpx: HTTP Request: GET http://127.0.0.1:7861/startup-events "HTTP/1.1 200 OK"
+[2024-07-23 16:09:00,859)](INFO)httpx: HTTP Request: HEAD http://127.0.0.1:7861/ "HTTP/1.1 200 OK"
+[2024-07-23 16:09:01,260)](INFO)httpx: HTTP Request: GET https://api.gradio.app/pkg-version "HTTP/1.1 200 OK"
+[2024-07-23 16:09:01,636)](INFO)httpx: HTTP Request: GET https://api.gradio.app/v2/tunnel-request "HTTP/1.1 200 OK"

DATA/ex-mnist/LOGS/2024-07-23_16-11-14_app.log

@@ -0,0 +1,131 @@
+[2024-07-23 16:11:14,890)](INFO)KadiAIProject: Logging to /mnt/data2/yinghanz/codes/frameworks/test/cids_gr_example/DATA/ex-mnist/LOGS/2024-07-23_16-11-14_app.log
+[2024-07-23 16:11:14,892)](INFO)KadiAIProject: Setting seeds to: ex-mnist
+[2024-07-23 16:11:14,895)](INFO)KadiAIProject: Loading from /mnt/data2/yinghanz/codes/frameworks/test/cids_gr_example/DATA/ex-mnist/project_ex-mnist.json
+[2024-07-23 16:11:16,958)](INFO)KadiAIProject: Git commit: 143d0296
+[2024-07-23 16:11:17,213)](INFO)CIDSModelTF: Single CPU found. Using one device distribution.
+[2024-07-23 16:11:17,218)](INFO)KadiAIProject: >> Evaluating...
+[2024-07-23 16:11:17,219)](INFO)KadiAIProject: >>> Metrics...
+[2024-07-23 16:11:17,404)](WARNING)CIDSModelTF: Attempting to load checkpoint='last' but model.save_best_only = True. This will not load the final model state at the end of training but the last checkpoint that improved the model.monitor = 'val_accuracy'. Use checkpoint='last_phase' to get final state.
+[2024-07-23 16:11:17,407)](INFO)CIDSModelTF: Continuing from epoch 16 (checkpoint 0000016).
+[2024-07-23 16:11:17,509)](INFO)CIDSModelTF: Model Summary: mnist--image--label--onehot-default-64C3-64C3-MP2-128C3-128C3-MP2-256C3-MP2-512-10
+[2024-07-23 16:11:17,510)](INFO)CIDSModelTF: Model: "InputPreprocess"
+[2024-07-23 16:11:17,510)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:11:17,511)](INFO)CIDSModelTF:  Layer (type)                Output Shape              Param #   
+[2024-07-23 16:11:17,512)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:11:17,512)](INFO)CIDSModelTF:  input_1 (InputLayer)        [(None, 28, 28, 1)]       0         
+[2024-07-23 16:11:17,513)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,514)](INFO)CIDSModelTF:  tf.identity (TFOpLambda)    (None, 28, 28, 1)         0         
+[2024-07-23 16:11:17,514)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,515)](INFO)CIDSModelTF:  tf.cast (TFOpLambda)        (None, 28, 28, 1)         0         
+[2024-07-23 16:11:17,515)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,516)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:11:17,517)](INFO)CIDSModelTF: Total params: 0
+[2024-07-23 16:11:17,517)](INFO)CIDSModelTF: Trainable params: 0
+[2024-07-23 16:11:17,518)](INFO)CIDSModelTF: Non-trainable params: 0
+[2024-07-23 16:11:17,518)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:11:17,519)](INFO)CIDSModelTF: Model: "OutputPreprocess"
+[2024-07-23 16:11:17,519)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:11:17,520)](INFO)CIDSModelTF:  Layer (type)                Output Shape              Param #   
+[2024-07-23 16:11:17,521)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:11:17,521)](INFO)CIDSModelTF:  input_2 (InputLayer)        [(None, 1)]               0         
+[2024-07-23 16:11:17,522)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,522)](INFO)CIDSModelTF:  tf.identity_1 (TFOpLambda)  (None, 1)                 0         
+[2024-07-23 16:11:17,523)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,523)](INFO)CIDSModelTF:  tf.__operators__.getitem (S  (None,)                  0         
+[2024-07-23 16:11:17,524)](INFO)CIDSModelTF:  licingOpLambda)                                                 
+[2024-07-23 16:11:17,524)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,525)](INFO)CIDSModelTF:  tf.cast_1 (TFOpLambda)      (None,)                   0         
+[2024-07-23 16:11:17,526)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,526)](INFO)CIDSModelTF:  tf.one_hot (TFOpLambda)     (None, 10)                0         
+[2024-07-23 16:11:17,527)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,527)](INFO)CIDSModelTF:  tf.cast_2 (TFOpLambda)      (None, 10)                0         
+[2024-07-23 16:11:17,528)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,528)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:11:17,529)](INFO)CIDSModelTF: Total params: 0
+[2024-07-23 16:11:17,529)](INFO)CIDSModelTF: Trainable params: 0
+[2024-07-23 16:11:17,530)](INFO)CIDSModelTF: Non-trainable params: 0
+[2024-07-23 16:11:17,531)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:11:17,531)](INFO)CIDSModelTF: Model: "Core"
+[2024-07-23 16:11:17,532)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:11:17,532)](INFO)CIDSModelTF:  Layer (type)                Output Shape              Param #   
+[2024-07-23 16:11:17,533)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:11:17,533)](INFO)CIDSModelTF:  input_3 (InputLayer)        [(None, 28, 28, 1)]       0         
+[2024-07-23 16:11:17,534)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,534)](INFO)CIDSModelTF:  conv2d (Conv2D)             (None, 28, 28, 64)        640       
+[2024-07-23 16:11:17,535)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,535)](INFO)CIDSModelTF:  re_lu (ReLU)                (None, 28, 28, 64)        0         
+[2024-07-23 16:11:17,536)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,537)](INFO)CIDSModelTF:  conv2d_1 (Conv2D)           (None, 28, 28, 64)        36928     
+[2024-07-23 16:11:17,537)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,538)](INFO)CIDSModelTF:  re_lu_1 (ReLU)              (None, 28, 28, 64)        0         
+[2024-07-23 16:11:17,538)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,539)](INFO)CIDSModelTF:  max_pooling2d (MaxPooling2D  (None, 14, 14, 64)       0         
+[2024-07-23 16:11:17,539)](INFO)CIDSModelTF:  )                                                               
+[2024-07-23 16:11:17,539)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,540)](INFO)CIDSModelTF:  batch_normalization (BatchN  (None, 14, 14, 64)       256       
+[2024-07-23 16:11:17,540)](INFO)CIDSModelTF:  ormalization)                                                   
+[2024-07-23 16:11:17,541)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,541)](INFO)CIDSModelTF:  conv2d_2 (Conv2D)           (None, 14, 14, 128)       73856     
+[2024-07-23 16:11:17,542)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,542)](INFO)CIDSModelTF:  re_lu_2 (ReLU)              (None, 14, 14, 128)       0         
+[2024-07-23 16:11:17,543)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,544)](INFO)CIDSModelTF:  conv2d_3 (Conv2D)           (None, 14, 14, 128)       147584    
+[2024-07-23 16:11:17,544)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,545)](INFO)CIDSModelTF:  re_lu_3 (ReLU)              (None, 14, 14, 128)       0         
+[2024-07-23 16:11:17,545)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,546)](INFO)CIDSModelTF:  max_pooling2d_1 (MaxPooling  (None, 7, 7, 128)        0         
+[2024-07-23 16:11:17,546)](INFO)CIDSModelTF:  2D)                                                             
+[2024-07-23 16:11:17,547)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,547)](INFO)CIDSModelTF:  batch_normalization_1 (Batc  (None, 7, 7, 128)        512       
+[2024-07-23 16:11:17,547)](INFO)CIDSModelTF:  hNormalization)                                                 
+[2024-07-23 16:11:17,548)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,548)](INFO)CIDSModelTF:  conv2d_4 (Conv2D)           (None, 7, 7, 256)         295168    
+[2024-07-23 16:11:17,549)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,550)](INFO)CIDSModelTF:  max_pooling2d_2 (MaxPooling  (None, 3, 3, 256)        0         
+[2024-07-23 16:11:17,550)](INFO)CIDSModelTF:  2D)                                                             
+[2024-07-23 16:11:17,550)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,551)](INFO)CIDSModelTF:  flatten (Flatten)           (None, 2304)              0         
+[2024-07-23 16:11:17,551)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,552)](INFO)CIDSModelTF:  dropout (Dropout)           (None, 2304)              0         
+[2024-07-23 16:11:17,552)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,553)](INFO)CIDSModelTF:  dense (Dense)               (None, 512)               1180160   
+[2024-07-23 16:11:17,553)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,554)](INFO)CIDSModelTF:  dense_1 (Dense)             (None, 10)                5130      
+[2024-07-23 16:11:17,554)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,555)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:11:17,555)](INFO)CIDSModelTF: Total params: 1,740,234
+[2024-07-23 16:11:17,556)](INFO)CIDSModelTF: Trainable params: 1,739,850
+[2024-07-23 16:11:17,556)](INFO)CIDSModelTF: Non-trainable params: 384
+[2024-07-23 16:11:17,557)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:11:17,557)](INFO)CIDSModelTF: Model: "Postprocess"
+[2024-07-23 16:11:17,557)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:11:17,558)](INFO)CIDSModelTF:  Layer (type)                Output Shape              Param #   
+[2024-07-23 16:11:17,558)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:11:17,559)](INFO)CIDSModelTF:  input_5 (InputLayer)        [(None, 10)]              0         
+[2024-07-23 16:11:17,559)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,560)](INFO)CIDSModelTF:  tf.math.argmax (TFOpLambda)  (None,)                  0         
+[2024-07-23 16:11:17,560)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,561)](INFO)CIDSModelTF:  tf.expand_dims (TFOpLambda)  (None, 1)                0         
+[2024-07-23 16:11:17,561)](INFO)CIDSModelTF:                                                                  
+[2024-07-23 16:11:17,562)](INFO)CIDSModelTF: =================================================================
+[2024-07-23 16:11:17,562)](INFO)CIDSModelTF: Total params: 0
+[2024-07-23 16:11:17,562)](INFO)CIDSModelTF: Trainable params: 0
+[2024-07-23 16:11:17,563)](INFO)CIDSModelTF: Non-trainable params: 0
+[2024-07-23 16:11:17,563)](INFO)CIDSModelTF: _________________________________________________________________
+[2024-07-23 16:11:17,565)](INFO)CIDSModelTF: Estimated memory (Core model): 0.012000 GBytes
+[2024-07-23 16:11:17,566)](INFO)CIDSModelTF: Estimated memory (Input preprocess model): 0.000000 GBytes
+[2024-07-23 16:11:17,566)](INFO)CIDSModelTF: Estimated memory (Output preprocess model): 0.000000 GBytes
+[2024-07-23 16:11:17,567)](INFO)CIDSModelTF: Estimated memory (Postprocess model): 0.000000 GBytes
+[2024-07-23 16:11:17,568)](INFO)DataReader: (test) Estimated sample size: 0.00309086 MB.
+[2024-07-23 16:11:17,568)](INFO)DataReader: (test) buffer_size set to maximum number of samples (1).
+[2024-07-23 16:11:17,954)](INFO)DataReader: (test) Caching dataset. Disable by setting model.data_reader.prefetch = True. Currently set to: "cache".
+[2024-07-23 16:11:17,960)](WARNING)DataReader: Batch size is larger than dataset. Repeating dataset and dropping remainder to achieve requested batch size.
+[2024-07-23 16:11:18,478)](INFO)CIDSModelTF: Preprocessing...
+[2024-07-23 16:11:18,774)](INFO)CIDSModelTF: Inferring...
+[2024-07-23 16:11:22,431)](INFO)CIDSModelTF: Postprocessing...
+[2024-07-23 16:11:22,695)](INFO)CIDSModelTF: Inference completed.
+[2024-07-23 16:11:22,887)](INFO)httpx: HTTP Request: GET https://checkip.amazonaws.com/ "HTTP/1.1 200 "
+[2024-07-23 16:11:23,163)](INFO)httpx: HTTP Request: GET http://127.0.0.1:7861/startup-events "HTTP/1.1 200 OK"
+[2024-07-23 16:11:23,193)](INFO)httpx: HTTP Request: HEAD http://127.0.0.1:7861/ "HTTP/1.1 200 OK"
+[2024-07-23 16:11:23,490)](INFO)httpx: HTTP Request: GET https://api.gradio.app/pkg-version "HTTP/1.1 200 OK"
+[2024-07-23 16:11:23,954)](INFO)httpx: HTTP Request: GET https://api.gradio.app/v2/tunnel-request "HTTP/1.1 200 OK"

DATA/ex-mnist/RESULTS/mnist--image--label--onehot/cidsmodel.json

@@ -0,0 +1,80 @@
+{
+    "key": "CIDS:Model",
+    "type": "dict",
+    "value": [
+        {
+            "key": "Name",
+            "type": "str",
+            "value": "mnist--image--label--onehot"
+        },
+        {
+            "key": "Hyperparameters",
+            "type": "dict",
+            "value": [
+                {
+                    "key": "Model",
+                    "type": "dict",
+                    "value": []
+                },
+                {
+                    "key": "Training",
+                    "type": "dict",
+                    "value": [
+                        {
+                            "key": "learning_rate",
+                            "type": "float",
+                            "validation": {
+                                "options": [
+                                    0.003,
+                                    0.001,
+                                    0.0003,
+                                    0.0001,
+                                    3e-05,
+                                    1e-05
+                                ],
+                                "required": true
+                            },
+                            "value": 0.0001
+                        },
+                        {
+                            "key": "num_kernels",
+                            "type": "int",
+                            "validation": {
+                                "options": [
+                                    32,
+                                    64,
+                                    128,
+                                    256,
+                                    512
+                                ],
+                                "required": true
+                            },
+                            "value": 64
+                        },
+                        {
+                            "key": "dropout",
+                            "type": "float",
+                            "validation": {
+                                "options": [
+                                    0.0,
+                                    0.07,
+                                    0.14,
+                                    0.21,
+                                    0.28,
+                                    0.3,
+                                    0.35,
+                                    0.42,
+                                    0.49,
+                                    0.56,
+                                    0.63
+                                ],
+                                "required": true
+                            },
+                            "value": 0.3
+                        }
+                    ]
+                }
+            ]
+        }
+    ]
+}

DATA/ex-mnist/RESULTS/mnist--image--label--onehot/mnist--image--label--onehot-default-64C3-64C3-MP2-128C3-128C3-MP2-256C3-MP2-512-10/checkpoint/0000016/input_preprocess_weights.h5

@@ -0,0 +1,3 @@
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+            "name": "InputPreprocess",
+            "output_layers": [
+                [
+                    "tf.cast",
+                    0,
+                    0
+                ]
+            ]
+        },
+        "output_preprocess_model": {
+            "input_layers": [
+                [
+                    "input_2",
+                    0,
+                    0
+                ]
+            ],
+            "layers": [
+                {
+                    "class_name": "InputLayer",
+                    "config": {
+                        "batch_input_shape": [
+                            null,
+                            1
+                        ],
+                        "dtype": "float32",
+                        "name": "input_2",
+                        "ragged": false,
+                        "sparse": false
+                    },
+                    "inbound_nodes": [],
+                    "name": "input_2"
+                },
+                {
+                    "class_name": "TFOpLambda",
+                    "config": {
+                        "dtype": "float32",
+                        "function": "identity",
+                        "name": "tf.identity_1",
+                        "trainable": true
+                    },
+                    "inbound_nodes": [
+                        [
+                            "input_2",
+                            0,
+                            0,
+                            {}
+                        ]
+                    ],
+                    "name": "tf.identity_1"
+                },
+                {
+                    "class_name": "SlicingOpLambda",
+                    "config": {
+                        "dtype": "float32",
+                        "function": "__operators__.getitem",
+                        "name": "tf.__operators__.getitem",
+                        "trainable": true
+                    },
+                    "inbound_nodes": [
+                        [
+                            "tf.identity_1",
+                            0,
+                            0,
+                            {
+                                "slice_spec": [
+                                    "Ellipsis",
+                                    0
+                                ]
+                            }
+                        ]
+                    ],
+                    "name": "tf.__operators__.getitem"
+                },
+                {
+                    "class_name": "TFOpLambda",
+                    "config": {
+                        "dtype": "float32",
+                        "function": "cast",
+                        "name": "tf.cast_1",
+                        "trainable": true
+                    },
+                    "inbound_nodes": [
+                        [
+                            "tf.__operators__.getitem",
+                            0,
+                            0,
+                            {
+                                "dtype": "tf.int32"
+                            }
+                        ]
+                    ],
+                    "name": "tf.cast_1"
+                },
+                {
+                    "class_name": "TFOpLambda",
+                    "config": {
+                        "dtype": "float32",
+                        "function": "one_hot",
+                        "name": "tf.one_hot",
+                        "trainable": true
+                    },
+                    "inbound_nodes": [
+                        [
+                            "tf.cast_1",
+                            0,
+                            0,
+                            {
+                                "depth": 10
+                            }
+                        ]
+                    ],
+                    "name": "tf.one_hot"
+                },
+                {
+                    "class_name": "TFOpLambda",
+                    "config": {
+                        "dtype": "float32",
+                        "function": "cast",
+                        "name": "tf.cast_2",
+                        "trainable": true
+                    },
+                    "inbound_nodes": [
+                        [
+                            "tf.one_hot",
+                            0,
+                            0,
+                            {
+                                "dtype": "tf.float32"
+                            }
+                        ]
+                    ],
+                    "name": "tf.cast_2"
+                }
+            ],
+            "name": "OutputPreprocess",
+            "output_layers": [
+                [
+                    "tf.cast_2",
+                    0,
+                    0
+                ]
+            ]
+        },
+        "postprocess_model": {
+            "input_layers": [
+                [
+                    "input_5",
+                    0,
+                    0
+                ]
+            ],
+            "layers": [
+                {
+                    "class_name": "InputLayer",
+                    "config": {
+                        "batch_input_shape": [
+                            null,
+                            10
+                        ],
+                        "dtype": "float32",
+                        "name": "input_5",
+                        "ragged": false,
+                        "sparse": false
+                    },
+                    "inbound_nodes": [],
+                    "name": "input_5"
+                },
+                {
+                    "class_name": "TFOpLambda",
+                    "config": {
+                        "dtype": "float32",
+                        "function": "math.argmax",
+                        "name": "tf.math.argmax",
+                        "trainable": true
+                    },
+                    "inbound_nodes": [
+                        [
+                            "input_5",
+                            0,
+                            0,
+                            {
+                                "axis": -1
+                            }
+                        ]
+                    ],
+                    "name": "tf.math.argmax"
+                },
+                {
+                    "class_name": "TFOpLambda",
+                    "config": {
+                        "dtype": "float32",
+                        "function": "expand_dims",
+                        "name": "tf.expand_dims",
+                        "trainable": true
+                    },
+                    "inbound_nodes": [
+                        [
+                            "tf.math.argmax",
+                            0,
+                            0,
+                            {
+                                "axis": -1
+                            }
+                        ]
+                    ],
+                    "name": "tf.expand_dims"
+                }
+            ],
+            "name": "Postprocess",
+            "output_layers": [
+                [
+                    "tf.expand_dims",
+                    0,
+                    0
+                ]
+            ]
+        },
+        "settings": {
+            "VERBOSITY": 2,
+            "_best_monitor": 0.9915555715560913,
+            "_callbacks": [
+                "<cids.tensorflow.callbacks.FreezeControl object at 0x7f61cc666230>"
+            ],
+            "_checkpoint_format_str": "{epoch:07d}",
+            "_collapse_repeated_features": false,
+            "_encode_categorical": "outputs",
+            "_hp": "<keras_tuner.src.engine.hyperparameters.hyperparameters.HyperParameters object at 0x7f6274542fe0>",
+            "_logger": "<Logger CIDSModelTF (INFO)>",
+            "_online_normalize": false,
+            "base_name": "mnist--image--label--onehot",
+            "built": true,
+            "built_input_shape": [
+                null,
+                28,
+                28,
+                1
+            ],
+            "built_output_shape": [
+                null,
+                1
+            ],
+            "built_submodels": null,
+            "conditional_inp": [],
+            "count": 21,
+            "count_mode": "epochs",
+            "data_definition": "<cids.data.definition.DataDefinition object at 0x7f62e05514b0>",
+            "data_reader": "<cids.data.reader.DataReader object at 0x7f61cc6661d0>",
+            "dtype": "tf.float32",
+            "identifier": "default",
+            "initial_count": 21,
+            "input_online_normalizer": null,
+            "loss_weights": [],
+            "meta_architecture": "serial",
+            "meta_folder": "",
+            "metrics": [
+                "accuracy"
+            ],
+            "monitor": "val_accuracy",
+            "num_classes": 10,
+            "num_gpus": 0,
+            "optimizer": null,
+            "optimizer_config": {},
+            "output_online_normalizer": null,
+            "parallel_inp": [],
+            "phase": 1,
+            "progress_callback": "<cids.tensorflow.callbacks.EpochProgressCallback object at 0x7f6288099c60>",
+            "report_freq": 1,
+            "report_samples_above_threshold": false,
+            "result_dir": "PosixPath('/mnt/data2/yinghanz/codes/frameworks/test/cids/DATA/ex-mnist/RESULTS')",
+            "save_best_only": true,
+            "save_freq": 1,
+            "states": [],
+            "strategy": "<tensorflow.python.distribute.one_device_strategy.OneDeviceStrategy object at 0x7f61cc6667d0>",
+            "suppress_architecture_string": false
+        }
+    },
+    "model_hyper_parameters": {
+        "dropout": 0.3,
+        "num_kernels": 64
+    }
+}

DATA/ex-mnist/RESULTS/mnist--image--label--onehot/mnist--image--label--onehot-default-64C3-64C3-MP2-128C3-128C3-MP2-256C3-MP2-512-10/summary/train/events.out.tfevents.1721741561.mms-hgx-01.3272949.0.v2

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+{
+    "train_loss": 0.31650257110595703,
+    "training_history": {
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+        "epochs": [
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+        "val_accuracy": [
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+        "count": 1,
+        "freeze": false,
+        "learning_rate": 0.0001
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+    "training_schedule": {
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+    "valid_loss": 3.1068718433380127,
+    "valid_loss_best": 3.1068718433380127
+}

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+{
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+        "count": 21,
+        "freeze": true,
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+    "training_schedule": {
+        "batch_size": 256,
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+            1,
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+            false,
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+        "learning_rate": 0.0001
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+    "valid_loss": 0.04104391485452652,
+    "valid_loss_best": 0.031171562150120735
+}

README.md

@@ -1,12 +1,14 @@
 ---
-title: Mnist Cids
-emoji: 🐢
-colorFrom: blue
-colorTo: blue
+title: MNIST-CIDS
+emoji: 😻
+colorFrom: red
+colorTo: indigo
 sdk: gradio
 sdk_version: 4.39.0
 app_file: app.py
 pinned: false
+python_version: 3.10.6
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+

app.py

@@ -0,0 +1,200 @@
+import numpy as np
+import cv2 as cv
+from urllib.request import urlretrieve
+import gradio as gr
+import matplotlib.pyplot as plt
+
+# urlretrieve("https://github.com/AyaanZaveri/mnist/raw/main/mnist-model.h5", "mnist-model.h5")
+
+# model = tf.keras.models.load_model("mnist-model.h5")
+
+
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
+
+import cids
+
+import numpy as np
+import tensorflow as tf
+import seaborn as sns
+
+from pathlib import Path
+from tensorflow.keras import layers as klayers
+from cids.tensorflow import layers as clayers
+from cids.tensorflow.tuner import SearchResults
+from cids.statistics import metrics
+from kadi_ai import KadiAIProject
+from matplotlib import pyplot as plt
+from kerastuner import HyperParameters
+
+
+################################################################################
+# Controls
+
+CHECK = False
+SEARCH = False
+USE_BEST_SEARCH_CONFIG = False
+TRAIN = False
+EVAL = True
+PLOT = False
+ANALYZE = False
+
+TRAIN_CONTINUE = False
+
+
+num_check_samples = 100
+num_plot_samples = 20
+num_principal_components = 3
+
+
+################################################################################
+# Data paths
+
+project_name = "ex-mnist"
+project_dir = Path.cwd() / "DATA" / project_name
+project = KadiAIProject(project_name, root=project_dir)
+
+# Read paths
+# train_samples, valid_samples, test_samples = project.get_split_datasets(
+#     shuffle=True, valid_split=0.15, test_split=0.15
+# )
+
+test_samples = ["./DATA/ex-mnist/INPUTS/tfrecord/sample29579.tfrecord"]
+
+
+
+################################################################################
+# Data definition
+
+data_definition = project.data_definition
+data_definition.input_features = ["image"]
+data_definition.output_features = ["label"]
+
+
+################################################################################
+# Neural network
+
+# Model
+def model_function(hp, data_definition):
+
+    # Hyper parameters
+    num_kernels = hp.Choice("num_kernels", [32, 64, 128, 256, 512], default=64)
+    dropout_rate = hp.Float("dropout", 0.0, 0.7, default=0.3)  # not used
+
+    # Ref: https://github.com/AyaanZaveri/mnist/blob/main/MNIST_Number.ipynb
+    layers = []
+    layers.append(klayers.Conv2D(num_kernels, (3, 3), strides=(1, 1), padding="same"))
+    layers.append(klayers.ReLU())
+    layers.append(klayers.Conv2D(num_kernels, (3, 3), strides=(1, 1), padding="same"))
+    layers.append(klayers.ReLU())
+    # layers.append(klayers.Dropout(dropout_rate))
+    layers.append(klayers.MaxPooling2D(pool_size=(2, 2)))
+    layers.append(klayers.BatchNormalization())
+    
+    layers.append(klayers.Conv2D(num_kernels*2, (3, 3), strides=(1, 1), padding="same"))
+    layers.append(klayers.ReLU())
+    layers.append(klayers.Conv2D(num_kernels*2, (3, 3), strides=(1, 1), padding="same"))
+    layers.append(klayers.ReLU())
+    
+    layers.append(klayers.MaxPooling2D(pool_size=(2, 2)))
+    layers.append(klayers.BatchNormalization())
+    
+    layers.append(klayers.Conv2D(num_kernels*4, (3, 3), strides=(1, 1), padding="same"))
+    layers.append(klayers.MaxPooling2D(pool_size=(2, 2)))
+    
+    layers.append(klayers.Flatten())
+    layers.append(klayers.Dropout(dropout_rate))
+    layers.append(klayers.Dense(512))
+    layers.append(klayers.Dense(10, activation="softmax"))
+    return tf.keras.Sequential(layers)
+
+
+# Set a model name
+model_name = "mnist"
+model_name += "--" + "--".join(
+    [
+        "+".join(list(data_definition.input_features)),
+        "+".join(list(data_definition.output_features)),
+    ]
+)
+model_name += "--onehot"
+
+saved_model_name = "mnist--image--label--onehot-default-64C3-64C3-MP2-128C3-128C3-MP2-256C3-MP2-512-10"
+
+model = cids.CIDSModel.categorical_classification(
+    10,
+    data_definition,
+    model_function,
+    name=model_name,
+    identifier="default",  # or "best"
+    result_dir=project.result_dir,
+)
+model.encode_categorical = "outputs"
+model.metrics.append("accuracy")
+model.monitor = "val_accuracy"
+model.online_normalize = False
+model.data_reader.prefetch = "cache"
+
+# Load hp
+import json
+hp_path = os.path.join(model.base_model_dir, saved_model_name, "hp.json")
+print("hp path", hp_path)
+with open(hp_path, "r") as f:
+    saved_config = json.load(f)
+hp = HyperParameters.from_config(saved_config)
+
+
+if EVAL:
+    project.log(">> Evaluating...")
+    project.log(">>> Metrics...")
+    # Compute predictions
+    # test_loss = model.eval_data(
+    #     test_samples, batch_size=4, checkpoint="last", hp=hp, submodels="generator")
+    print("hp", hp.values)
+    X, Y, Y_ = model.infer_data(
+        test_samples,
+        batch_size=4,
+        checkpoint="last",
+        hp=hp,
+    )
+
+model = model.core_model
+
+def recognize_digit(image):
+    image = cv.resize(image["composite"][:,:,-1], (28, 28))
+    # For debug, find out which chanel
+    # from PIL import Image
+    # for i in range(images["composite"].shape[-1]):
+    #     image = images["composite"][:,:,i]
+    #     im = Image.fromarray(image)
+    #     im.save(f"c{i}.png")
+    # for i in range(images["background"].shape[-1]):
+    #     image = images["background"][:,:,i]
+    #     im = Image.fromarray(image)
+    #     im.save(f"b{i}.png")
+    # print(image.shape)
+    # # image = image / 255
+    # # plt.imshow(image)
+    
+    #from PIL import Image
+    #im = Image.fromarray(image)
+    #im.save(f"saved.png")
+    
+    # image = image / 255
+    #print("max", image.max())
+    image = (image - 127.5) / 127.5
+    image = image.reshape((1, 28, 28))
+    prediction = model.predict(image)
+    
+    prediction = model.predict(image).tolist()[0]
+    return {str(i): prediction[i] for i in range(10)}
+
+gr.Interface(fn=recognize_digit, 
+             inputs="sketchpad", 
+             outputs=gr.Label(num_top_classes=3),
+             live=True,
+             css=".footer {display:none !important}",
+             # title="MNIST Sketchpad",
+             description="A simple model trained on MNIST dataset using CIDS framework.\nDraw a single digit (0-9) in the center of the canvas.").launch(share=True)

requirements.txt

@@ -0,0 +1,3 @@
+git+https://gitlab.com/intelligent-analysis/cids.git@develop
+opencv-python
+numpy==1.26