#!pip install ANNarchyANN-to-SNN conversion - MLP
This notebook demonstrates how to transform a fully-connected neural network trained using tensorflow/keras into an SNN network usable in ANNarchy.
The methods are adapted from the original models used in:
Diehl et al. (2015) “Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing” Proceedings of IJCNN. doi: 10.1109/IJCNN.2015.7280696
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
print(f"Tensorflow {tf.__version__}")2026-01-05 13:47:55.565472: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2026-01-05 13:47:55.567856: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.
2026-01-05 13:47:55.576830: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1767617275.592134 120261 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1767617275.596719 120261 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2026-01-05 13:47:55.611751: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.Tensorflow 2.18.0First we need to download and process the MNIST dataset provided by tensorflow.
# Download data
(X_train, t_train), (X_test, t_test) = tf.keras.datasets.mnist.load_data()
# Normalize inputs
X_train = X_train.reshape(X_train.shape[0], 784).astype('float32') / 255.
X_test = X_test.reshape(X_test.shape[0], 784).astype('float32') / 255.
# One-hot output vectors
T_train = tf.keras.utils.to_categorical(t_train, 10)
T_test = tf.keras.utils.to_categorical(t_test, 10)Training an ANN in tensorflow/keras
The tensorflow.keras network is build using the functional API.
The fully-connected network has two fully connected layers with ReLU, no bias, dropout at 0.5, and a softmax output layer with 10 neurons. We use the standard SGD optimizer and the categorical crossentropy loss for classification.
def create_mlp():
# Model
inputs = tf.keras.layers.Input(shape=(784,))
x= tf.keras.layers.Dense(128, use_bias=False, activation='relu')(inputs)
x = tf.keras.layers.Dropout(0.5)(x)
x= tf.keras.layers.Dense(128, use_bias=False, activation='relu')(x)
x = tf.keras.layers.Dropout(0.5)(x)
x=tf.keras.layers.Dense(10, use_bias=False, activation='softmax')(x)
model= tf.keras.Model(inputs, x)
# Optimizer
optimizer = tf.keras.optimizers.SGD(learning_rate=0.05)
# Loss function
model.compile(
loss='categorical_crossentropy', # loss function
optimizer=optimizer, # learning rule
metrics=['accuracy'] # show accuracy
)
print(model.summary())
return modelWe can now train the network and save the weights in the HDF5 format.
# Create model
model = create_mlp()
# Train model
history = model.fit(
X_train, T_train, # training data
batch_size=128, # batch size
epochs=20, # Maximum number of epochs
validation_split=0.1, # Percentage of training data used for validation
)
model.save("runs/mlp.keras")
# Test model
predictions_keras = model.predict(X_test, verbose=0)
test_loss, test_accuracy = model.evaluate(X_test, T_test, verbose=0)
print(f"Test accuracy: {test_accuracy}")W0000 00:00:1767617277.238554 120261 gpu_device.cc:2344] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...Model: "functional"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ input_layer (InputLayer) │ (None, 784) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ (None, 128) │ 100,352 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dropout (Dropout) │ (None, 128) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_1 (Dense) │ (None, 128) │ 16,384 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dropout_1 (Dropout) │ (None, 128) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense_2 (Dense) │ (None, 10) │ 1,280 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 118,016 (461.00 KB)
Trainable params: 118,016 (461.00 KB)
Non-trainable params: 0 (0.00 B)
None
Epoch 1/202026-01-05 13:47:57.342668: W external/local_xla/xla/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 169344000 exceeds 10% of free system memory.2/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.0957 - loss: 2.5156 3/422 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.0968 - loss: 2.5156 1/422 ━━━━━━━━━━━━━━━━━━━━ 2:17 327ms/step - accuracy: 0.0938 - loss: 2.5156 4/422 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.0994 - loss: 2.5008 28/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.1424 - loss: 2.3581 29/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.1442 - loss: 2.3539 54/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.1849 - loss: 2.2660 55/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.1865 - loss: 2.2627 81/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.2263 - loss: 2.1781 107/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.2604 - loss: 2.1003 108/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.2616 - loss: 2.0946 135/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.2923 - loss: 2.0225 161/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3184 - loss: 1.9562 162/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3194 - loss: 1.9514 188/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3424 - loss: 1.8942 189/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3432 - loss: 1.8920 216/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3645 - loss: 1.8360 217/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3652 - loss: 1.8340 245/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.3849 - loss: 1.7817 272/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.4021 - loss: 1.7358 299/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.4178 - loss: 1.6937 325/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.4318 - loss: 1.6561 326/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.4323 - loss: 1.6547 352/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.4452 - loss: 1.6199 378/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.4572 - loss: 1.5875 404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.4684 - loss: 1.5572 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.4761 - loss: 1.5363 - val_accuracy: 0.9158 - val_loss: 0.3259 Epoch 2/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.8281 - loss: 0.5780 26/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8183 - loss: 0.6324 54/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8183 - loss: 0.6237 82/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8180 - loss: 0.6206 83/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8180 - loss: 0.6204 111/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8185 - loss: 0.6157 140/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8187 - loss: 0.6127 169/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8190 - loss: 0.6093 170/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8190 - loss: 0.6091 199/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8194 - loss: 0.6063 200/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8194 - loss: 0.6062 228/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8200 - loss: 0.6033 229/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8200 - loss: 0.6032 258/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8206 - loss: 0.6004 259/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8207 - loss: 0.6004 286/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8213 - loss: 0.5979 313/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8219 - loss: 0.5955 341/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8225 - loss: 0.5933 366/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8231 - loss: 0.5914 392/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8237 - loss: 0.5893 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8243 - loss: 0.5873 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8244 - loss: 0.5868 - val_accuracy: 0.9348 - val_loss: 0.2325 Epoch 3/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.8750 - loss: 0.4475 2/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8750 - loss: 0.4299 28/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8575 - loss: 0.4557 53/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8573 - loss: 0.4656 54/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8573 - loss: 0.4658 80/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8577 - loss: 0.4701 105/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8578 - loss: 0.4715 131/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8581 - loss: 0.4718 132/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8581 - loss: 0.4718 158/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8584 - loss: 0.4715 185/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8588 - loss: 0.4705 186/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8588 - loss: 0.4705 212/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8592 - loss: 0.4693 238/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8596 - loss: 0.4683 239/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8596 - loss: 0.4682 264/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8599 - loss: 0.4672 289/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8602 - loss: 0.4663 315/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8605 - loss: 0.4655 341/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8607 - loss: 0.4646 342/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8607 - loss: 0.4645 368/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8610 - loss: 0.4636 394/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8613 - loss: 0.4626 418/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8616 - loss: 0.4617 419/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8616 - loss: 0.4617 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8616 - loss: 0.4615 - val_accuracy: 0.9427 - val_loss: 0.1960 Epoch 4/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.9062 - loss: 0.3988 27/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8712 - loss: 0.4315 28/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8711 - loss: 0.4315 54/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8694 - loss: 0.4346 81/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8701 - loss: 0.4320 107/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8709 - loss: 0.4293 134/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8718 - loss: 0.4268 160/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8728 - loss: 0.4242 186/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8736 - loss: 0.4217 211/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8743 - loss: 0.4198 212/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8743 - loss: 0.4198 238/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8749 - loss: 0.4180 239/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8750 - loss: 0.4179 265/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8755 - loss: 0.4165 266/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8755 - loss: 0.4165 292/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8760 - loss: 0.4153 318/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8763 - loss: 0.4144 319/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8763 - loss: 0.4143 345/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8767 - loss: 0.4136 370/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8770 - loss: 0.4128 371/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8770 - loss: 0.4128 396/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8773 - loss: 0.4120 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8776 - loss: 0.4113 - val_accuracy: 0.9502 - val_loss: 0.1728 Epoch 5/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 9s 24ms/step - accuracy: 0.9141 - loss: 0.2683 26/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8938 - loss: 0.3643 54/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8918 - loss: 0.3684 81/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8909 - loss: 0.3711 82/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8908 - loss: 0.3712 109/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8903 - loss: 0.3735 136/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8901 - loss: 0.3749 163/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8900 - loss: 0.3751 189/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8902 - loss: 0.3742 190/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8902 - loss: 0.3742 217/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8903 - loss: 0.3733 243/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8905 - loss: 0.3725 269/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8907 - loss: 0.3717 296/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8909 - loss: 0.3709 322/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8911 - loss: 0.3702 347/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8913 - loss: 0.3696 348/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8913 - loss: 0.3696 375/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8915 - loss: 0.3690 401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8917 - loss: 0.3685 402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8917 - loss: 0.3685 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8918 - loss: 0.3680 - val_accuracy: 0.9540 - val_loss: 0.1566 Epoch 6/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.8828 - loss: 0.3283 2/422 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.8867 - loss: 0.3212 27/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8877 - loss: 0.3659 28/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8879 - loss: 0.3659 54/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8904 - loss: 0.3668 55/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8905 - loss: 0.3667 82/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8924 - loss: 0.3633 108/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8934 - loss: 0.3608 109/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8935 - loss: 0.3608 136/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8944 - loss: 0.3584 137/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8945 - loss: 0.3584 164/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8952 - loss: 0.3563 191/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8959 - loss: 0.3545 192/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8959 - loss: 0.3543 218/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8966 - loss: 0.3529 219/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8966 - loss: 0.3528 244/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8971 - loss: 0.3516 245/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8971 - loss: 0.3516 270/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8976 - loss: 0.3504 295/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8979 - loss: 0.3493 320/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8983 - loss: 0.3483 345/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8986 - loss: 0.3474 370/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8989 - loss: 0.3465 397/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8991 - loss: 0.3457 398/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8991 - loss: 0.3457 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8994 - loss: 0.3449 - val_accuracy: 0.9572 - val_loss: 0.1472 Epoch 7/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.9219 - loss: 0.3495 27/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9126 - loss: 0.3071 53/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9128 - loss: 0.3051 54/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9127 - loss: 0.3052 80/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9120 - loss: 0.3051 81/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9120 - loss: 0.3051 106/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9116 - loss: 0.3050 132/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9113 - loss: 0.3047 155/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9111 - loss: 0.3049 181/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9108 - loss: 0.3051 207/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9106 - loss: 0.3055 208/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9106 - loss: 0.3055 233/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9104 - loss: 0.3060 234/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9104 - loss: 0.3060 259/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9102 - loss: 0.3063 260/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9102 - loss: 0.3063 285/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9101 - loss: 0.3065 286/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9101 - loss: 0.3065 312/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9100 - loss: 0.3066 339/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9099 - loss: 0.3068 366/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9099 - loss: 0.3070 392/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9099 - loss: 0.3071 417/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9099 - loss: 0.3073 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9099 - loss: 0.3073 - val_accuracy: 0.9625 - val_loss: 0.1319 Epoch 8/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.9062 - loss: 0.3145 25/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9063 - loss: 0.3300 49/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9086 - loss: 0.3216 74/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9096 - loss: 0.3164 75/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9096 - loss: 0.3162 101/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9103 - loss: 0.3123 102/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9104 - loss: 0.3121 127/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9108 - loss: 0.3099 152/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9113 - loss: 0.3080 153/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9113 - loss: 0.3080 177/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9117 - loss: 0.3065 178/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9117 - loss: 0.3064 203/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9120 - loss: 0.3054 228/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9122 - loss: 0.3042 254/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9125 - loss: 0.3031 255/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9125 - loss: 0.3031 281/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9127 - loss: 0.3023 306/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9128 - loss: 0.3015 331/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9130 - loss: 0.3009 358/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9132 - loss: 0.3003 359/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9132 - loss: 0.3002 385/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9133 - loss: 0.2998 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9134 - loss: 0.2993 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9134 - loss: 0.2991 - val_accuracy: 0.9658 - val_loss: 0.1267 Epoch 9/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.9297 - loss: 0.1641 26/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9277 - loss: 0.2583 27/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9277 - loss: 0.2586 54/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9258 - loss: 0.2675 55/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9257 - loss: 0.2678 82/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9237 - loss: 0.2729 83/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9237 - loss: 0.2730 111/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9220 - loss: 0.2767 112/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9220 - loss: 0.2768 139/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9210 - loss: 0.2786 140/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9209 - loss: 0.2786 167/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9202 - loss: 0.2800 168/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9202 - loss: 0.2801 194/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9199 - loss: 0.2806 220/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9196 - loss: 0.2811 246/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9193 - loss: 0.2813 247/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9193 - loss: 0.2813 273/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9191 - loss: 0.2813 300/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9189 - loss: 0.2813 301/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9189 - loss: 0.2813 302/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9189 - loss: 0.2813 326/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9188 - loss: 0.2813 352/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9187 - loss: 0.2812 353/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9187 - loss: 0.2812 379/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9187 - loss: 0.2812 380/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9187 - loss: 0.2812 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9186 - loss: 0.2812 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9186 - loss: 0.2812 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9186 - loss: 0.2812 - val_accuracy: 0.9662 - val_loss: 0.1223 Epoch 10/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 9s 23ms/step - accuracy: 0.9141 - loss: 0.2579 2/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9062 - loss: 0.2691 28/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9088 - loss: 0.2768 56/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9122 - loss: 0.2780 57/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9123 - loss: 0.2778 85/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9146 - loss: 0.2755 112/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9160 - loss: 0.2733 113/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9161 - loss: 0.2732 140/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9170 - loss: 0.2716 167/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9177 - loss: 0.2705 195/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9183 - loss: 0.2696 196/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9183 - loss: 0.2696 221/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9186 - loss: 0.2691 222/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9186 - loss: 0.2691 249/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9189 - loss: 0.2689 275/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9191 - loss: 0.2687 301/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9194 - loss: 0.2685 302/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9194 - loss: 0.2684 328/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9196 - loss: 0.2683 329/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9196 - loss: 0.2683 353/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9198 - loss: 0.2682 378/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9198 - loss: 0.2682 379/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9199 - loss: 0.2682 380/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9199 - loss: 0.2682 405/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9200 - loss: 0.2681 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9200 - loss: 0.2682 - val_accuracy: 0.9678 - val_loss: 0.1169 Epoch 11/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.9141 - loss: 0.2586 26/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9312 - loss: 0.2390 51/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9296 - loss: 0.2418 52/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9295 - loss: 0.2419 78/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9283 - loss: 0.2446 105/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9270 - loss: 0.2477 133/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9262 - loss: 0.2504 160/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9259 - loss: 0.2518 187/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9256 - loss: 0.2529 188/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9256 - loss: 0.2529 214/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9254 - loss: 0.2535 241/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9253 - loss: 0.2541 268/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9251 - loss: 0.2548 294/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9250 - loss: 0.2556 320/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9248 - loss: 0.2563 321/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9248 - loss: 0.2563 349/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9247 - loss: 0.2568 376/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9247 - loss: 0.2572 402/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9246 - loss: 0.2575 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9246 - loss: 0.2577 - val_accuracy: 0.9683 - val_loss: 0.1125 Epoch 12/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.9141 - loss: 0.4034 2/422 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9199 - loss: 0.3629 28/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9235 - loss: 0.2972 54/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9241 - loss: 0.2869 80/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9236 - loss: 0.2828 107/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9236 - loss: 0.2792 134/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9238 - loss: 0.2760 161/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9242 - loss: 0.2730 162/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9242 - loss: 0.2729 189/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9246 - loss: 0.2705 214/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9248 - loss: 0.2689 215/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9248 - loss: 0.2688 243/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9250 - loss: 0.2672 269/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9252 - loss: 0.2659 270/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9252 - loss: 0.2659 297/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9253 - loss: 0.2647 298/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9253 - loss: 0.2647 323/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9254 - loss: 0.2638 349/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9255 - loss: 0.2630 375/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9256 - loss: 0.2622 401/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9257 - loss: 0.2616 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9258 - loss: 0.2612 - val_accuracy: 0.9693 - val_loss: 0.1074 Epoch 13/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.9297 - loss: 0.2320 25/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9247 - loss: 0.2466 50/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9238 - loss: 0.2506 75/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9251 - loss: 0.2499 101/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9258 - loss: 0.2494 126/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9266 - loss: 0.2485 151/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9271 - loss: 0.2478 152/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9271 - loss: 0.2477 178/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9276 - loss: 0.2470 204/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9278 - loss: 0.2466 230/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9280 - loss: 0.2465 231/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9280 - loss: 0.2465 257/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9280 - loss: 0.2467 284/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9280 - loss: 0.2469 310/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9281 - loss: 0.2470 338/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9281 - loss: 0.2471 364/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9282 - loss: 0.2471 365/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9282 - loss: 0.2471 366/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9282 - loss: 0.2471 393/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9283 - loss: 0.2470 394/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9283 - loss: 0.2470 420/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9284 - loss: 0.2469 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9284 - loss: 0.2469 - val_accuracy: 0.9713 - val_loss: 0.1021 Epoch 14/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.9219 - loss: 0.2012 26/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9373 - loss: 0.2376 53/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9370 - loss: 0.2354 81/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9364 - loss: 0.2338 82/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9364 - loss: 0.2337 109/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9362 - loss: 0.2323 136/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9356 - loss: 0.2323 162/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9353 - loss: 0.2322 188/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9350 - loss: 0.2318 213/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9349 - loss: 0.2315 238/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9348 - loss: 0.2314 263/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9346 - loss: 0.2315 290/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9344 - loss: 0.2317 315/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9343 - loss: 0.2318 316/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9342 - loss: 0.2318 341/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9341 - loss: 0.2319 342/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9341 - loss: 0.2319 370/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9339 - loss: 0.2322 398/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9337 - loss: 0.2324 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9336 - loss: 0.2326 - val_accuracy: 0.9710 - val_loss: 0.1041 Epoch 15/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 9s 22ms/step - accuracy: 0.9453 - loss: 0.2200 2/422 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 0.9395 - loss: 0.2321 29/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9274 - loss: 0.2644 30/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9275 - loss: 0.2641 56/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9278 - loss: 0.2569 82/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9279 - loss: 0.2536 110/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9280 - loss: 0.2513 111/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9280 - loss: 0.2512 139/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9281 - loss: 0.2488 166/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9283 - loss: 0.2470 194/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9286 - loss: 0.2455 195/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9286 - loss: 0.2454 222/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9288 - loss: 0.2443 249/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9290 - loss: 0.2433 250/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9290 - loss: 0.2432 276/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9292 - loss: 0.2423 303/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9295 - loss: 0.2414 304/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9295 - loss: 0.2413 331/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9297 - loss: 0.2405 357/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9299 - loss: 0.2397 358/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9299 - loss: 0.2396 385/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9300 - loss: 0.2390 411/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9301 - loss: 0.2384 412/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9301 - loss: 0.2383 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9302 - loss: 0.2381 - val_accuracy: 0.9718 - val_loss: 0.0983 Epoch 16/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.9844 - loss: 0.0926 26/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9325 - loss: 0.2142 27/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9323 - loss: 0.2145 53/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9311 - loss: 0.2158 80/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9319 - loss: 0.2141 106/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9324 - loss: 0.2142 133/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9326 - loss: 0.2152 160/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9327 - loss: 0.2165 186/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9327 - loss: 0.2174 213/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9329 - loss: 0.2179 214/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9329 - loss: 0.2179 240/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9331 - loss: 0.2181 265/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9333 - loss: 0.2182 266/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9333 - loss: 0.2182 292/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9335 - loss: 0.2184 293/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9335 - loss: 0.2185 318/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9336 - loss: 0.2187 319/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9336 - loss: 0.2187 345/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9338 - loss: 0.2189 371/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9339 - loss: 0.2191 372/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9339 - loss: 0.2192 398/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9340 - loss: 0.2193 400/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9340 - loss: 0.2193 399/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9340 - loss: 0.2193 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9341 - loss: 0.2194 - val_accuracy: 0.9715 - val_loss: 0.0976 Epoch 17/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.9141 - loss: 0.1960 27/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9272 - loss: 0.2164 28/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9274 - loss: 0.2163 55/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9309 - loss: 0.2161 81/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9321 - loss: 0.2182 82/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9321 - loss: 0.2183 108/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9328 - loss: 0.2198 109/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9328 - loss: 0.2198 136/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9334 - loss: 0.2202 164/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9338 - loss: 0.2209 191/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9340 - loss: 0.2214 217/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9343 - loss: 0.2215 243/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9345 - loss: 0.2213 244/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9345 - loss: 0.2213 271/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9348 - loss: 0.2210 272/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9348 - loss: 0.2210 299/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9350 - loss: 0.2208 325/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9352 - loss: 0.2206 352/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9354 - loss: 0.2204 378/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9355 - loss: 0.2202 403/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9356 - loss: 0.2199 404/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9356 - loss: 0.2199 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9357 - loss: 0.2198 - val_accuracy: 0.9737 - val_loss: 0.0942 Epoch 18/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 8s 20ms/step - accuracy: 0.9297 - loss: 0.2046 28/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9403 - loss: 0.1925 56/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9418 - loss: 0.1933 57/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9419 - loss: 0.1934 85/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9416 - loss: 0.1971 86/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9416 - loss: 0.1972 113/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9412 - loss: 0.2002 114/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9412 - loss: 0.2002 141/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9408 - loss: 0.2022 168/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9406 - loss: 0.2033 195/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9404 - loss: 0.2042 196/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9404 - loss: 0.2043 223/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9402 - loss: 0.2052 250/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9400 - loss: 0.2062 276/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9398 - loss: 0.2071 303/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9396 - loss: 0.2079 304/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9396 - loss: 0.2079 331/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9395 - loss: 0.2084 358/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9394 - loss: 0.2089 384/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9393 - loss: 0.2092 409/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9393 - loss: 0.2094 410/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9393 - loss: 0.2095 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9392 - loss: 0.2096 - val_accuracy: 0.9748 - val_loss: 0.0913 Epoch 19/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 8s 21ms/step - accuracy: 0.9688 - loss: 0.1111 26/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9472 - loss: 0.1837 27/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9470 - loss: 0.1842 53/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9439 - loss: 0.1896 80/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9421 - loss: 0.1938 81/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9420 - loss: 0.1940 108/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9408 - loss: 0.1983 134/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9401 - loss: 0.2011 161/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9396 - loss: 0.2030 162/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9396 - loss: 0.2031 189/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9393 - loss: 0.2043 190/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9393 - loss: 0.2043 218/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9392 - loss: 0.2051 245/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9392 - loss: 0.2056 246/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9392 - loss: 0.2056 272/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9393 - loss: 0.2058 298/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9393 - loss: 0.2059 299/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9393 - loss: 0.2059 300/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9393 - loss: 0.2059 326/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9394 - loss: 0.2059 352/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9394 - loss: 0.2060 353/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9394 - loss: 0.2060 380/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9394 - loss: 0.2061 406/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9394 - loss: 0.2061 407/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9394 - loss: 0.2061 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9395 - loss: 0.2060 - val_accuracy: 0.9740 - val_loss: 0.0925 Epoch 20/20 1/422 ━━━━━━━━━━━━━━━━━━━━ 9s 21ms/step - accuracy: 0.9453 - loss: 0.2159 26/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9358 - loss: 0.2080 27/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9357 - loss: 0.2081 54/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9360 - loss: 0.2116 80/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9362 - loss: 0.2111 81/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9363 - loss: 0.2111 107/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9366 - loss: 0.2107 133/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9368 - loss: 0.2107 160/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9370 - loss: 0.2105 161/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9370 - loss: 0.2105 188/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9372 - loss: 0.2102 189/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9372 - loss: 0.2102 217/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9374 - loss: 0.2100 244/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9375 - loss: 0.2097 245/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9376 - loss: 0.2097 272/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9377 - loss: 0.2095 299/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9378 - loss: 0.2092 326/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9379 - loss: 0.2091 327/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9379 - loss: 0.2091 353/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9380 - loss: 0.2089 354/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9380 - loss: 0.2089 381/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9381 - loss: 0.2088 408/422 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9382 - loss: 0.2086 422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9383 - loss: 0.2085 - val_accuracy: 0.9747 - val_loss: 0.0900 Test accuracy: 0.964900016784668
plt.figure(figsize=(12, 6))
plt.subplot(121)
plt.plot(history.history['loss'], '-r', label="Training")
plt.plot(history.history['val_loss'], '-b', label="Validation")
plt.xlabel('Epoch #')
plt.ylabel('Loss')
plt.legend()
plt.subplot(122)
plt.plot(history.history['accuracy'], '-r', label="Training")
plt.plot(history.history['val_accuracy'], '-b', label="Validation")
plt.xlabel('Epoch #')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
Initialize the ANN-to-SNN converter
We first create an instance of the ANN-to-SNN conversion object. The function receives the input_encoding parameter, which is the type of input encoding we want to use.
By default, there are intrinsically bursting (IB), phase shift oscillation (PSO) and Poisson (poisson) available.
from ANNarchy.extensions.ann_to_snn_conversion import ANNtoSNNConverter
snn_converter = ANNtoSNNConverter(
input_encoding='IB',
hidden_neuron='IaF',
read_out='spike_count',
)ANNarchy 5.0 (5.0.0) on linux (posix).After that, we provide the TensorFlow model stored as a .keras file to the conversion tool. The print-out of the network structure of the imported network is suppressed when show_info=False is provided to load_keras_model.
net = snn_converter.load_keras_model("runs/mlp.keras", show_info=True)WARNING: Dense representation is an experimental feature for spiking models, we greatly appreciate bug reports.
* Input layer: input_layer, (784,)
* InputLayer skipped.
* Dense layer: dense, 128
weights: (128, 784)
mean -0.0036564269103109837, std 0.05264626443386078
min -0.3581183850765228, max 0.2122010588645935
* Dropout skipped.
* Dense layer: dense_1, 128
weights: (128, 128)
mean 0.003981234040111303, std 0.10197019577026367
min -0.28383105993270874, max 0.3577111065387726
* Dropout skipped.
* Dense layer: dense_2, 10
weights: (10, 128)
mean -0.0034574796445667744, std 0.2152443379163742
min -0.5003451108932495, max 0.4554655849933624
When the network has been built successfully, we can perform a test using all MNIST training samples. Using duration_per_sample, the duration simulated for each image can be specified. Here, 200 ms seem to be enough.
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98%|█████████████████████████████████████████████████████████████████████████▊ | 9848/10000 [00:25<00:00, 412.62it/s] 99%|██████████████████████████████████████████████████████████████████████████▏| 9894/10000 [00:25<00:00, 425.53it/s] 99%|██████████████████████████████████████████████████████████████████████████▌| 9937/10000 [00:25<00:00, 425.94it/s]100%|██████████████████████████████████████████████████████████████████████████▊| 9980/10000 [00:25<00:00, 426.61it/s]100%|██████████████████████████████████████████████████████████████████████████| 10000/10000 [00:25<00:00, 393.10it/s]Using the recorded predictions, we can now compute the accuracy using scikit-learn for all presented samples.
from sklearn.metrics import classification_report, accuracy_score
print(classification_report(t_test, predictions_snn))
print("Test accuracy of the SNN:", accuracy_score(t_test, predictions_snn)) precision recall f1-score support
0 0.97 0.99 0.98 980
1 0.98 0.98 0.98 1135
2 0.96 0.96 0.96 1032
3 0.96 0.96 0.96 1010
4 0.97 0.94 0.96 982
5 0.97 0.95 0.96 892
6 0.96 0.97 0.97 958
7 0.96 0.96 0.96 1028
8 0.96 0.95 0.95 974
9 0.94 0.95 0.95 1009
accuracy 0.96 10000
macro avg 0.96 0.96 0.96 10000
weighted avg 0.96 0.96 0.96 10000
Test accuracy of the SNN: 0.9625For comparison, here is the performance of the original ANN in keras:
print(classification_report(t_test, predictions_keras.argmax(axis=1)))
print("Test accuracy of the ANN:", accuracy_score(t_test, predictions_keras.argmax(axis=1))) precision recall f1-score support
0 0.97 0.99 0.98 980
1 0.98 0.99 0.99 1135
2 0.96 0.97 0.96 1032
3 0.96 0.96 0.96 1010
4 0.96 0.95 0.96 982
5 0.97 0.94 0.95 892
6 0.96 0.97 0.97 958
7 0.96 0.96 0.96 1028
8 0.97 0.96 0.96 974
9 0.95 0.95 0.95 1009
accuracy 0.96 10000
macro avg 0.96 0.96 0.96 10000
weighted avg 0.96 0.96 0.96 10000
Test accuracy of the ANN: 0.9649