from data import MNIST
if __name__ == '__main__':
# gradients requires no eager execution.
tf.compat.v1.disable_eager_execution()
# Check for GPUs and set them to dynamically grow memory as needed
# Avoids OOM from tensorflow greedily allocating GPU memory
utils.gpu_dynamic_mem_growth()
x_train, y_train, _, _ = MNIST.get_mnist_data()
# (60000, 28, 28, 1) to ((60000, 28, 28)
# LSTM has (batch, time_steps, input_dim)
x_train = x_train.squeeze()
model = Sequential()
model.add(LSTM(16, input_shape=(28, 28)))
model.add(Dense(MNIST.num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
utils.print_names_and_shapes(keract.get_activations(model, x_train[:128]))
utils.print_names_and_shapes(
keract.get_gradients_of_trainable_weights(model, x_train[:128],
y_train[:128]))
utils.print_names_and_shapes(
keract.get_gradients_of_activations(model, x_train[:128],
y_train[:128]))
model = load_model(checkpoint_file)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
print(model.summary())
x_train, y_train, x_test, y_test = get_mnist_data()
# checking that the accuracy is the same as before 99% at the first epoch.
# test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0, batch_size=128)
# print('')
# assert test_acc > 0.98
utils.print_names_and_shapes(get_activations(
model, x_test[0:200])) # with 200 samples.
a = get_activations(model, x_test[0:1]) # with just one sample.
display_activations(a)
# import numpy as np
# import matplotlib.pyplot as plt
# plt.imshow(np.squeeze(x_test[0:1]), interpolation='None', cmap='gray')
else:
x_train, y_train, x_test, y_test = get_mnist_data()
model = Sequential()
model.add(
Conv2D(32,
kernel_size=(3, 3),
activation='relu',
import keras
from keras.layers import Dense
from keras.layers.recurrent import LSTM
from keras.models import Sequential
import utils
from data import get_mnist_data, num_classes
from keract import get_activations
if __name__ == '__main__':
x_train, _, _, _ = get_mnist_data()
# (60000, 28, 28, 1) to ((60000, 28, 28)
# LSTM has (batch, time_steps, input_dim)
x_train = x_train.squeeze()
model = Sequential()
model.add(LSTM(16, input_shape=(28, 28)))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
utils.print_names_and_shapes(get_activations(model, x_train[:128]))
model = load_model(checkpoint_file)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
print(model.summary())
x_train, y_train, x_test, y_test = MNIST.get_mnist_data()
# checking that the accuracy is the same as before 99% at the first epoch.
# test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0, batch_size=128)
# print('')
# assert test_acc > 0.98
utils.print_names_and_shapes(
keract.get_activations(model, x_test[0:200])) # with 200 samples.
utils.print_names_and_shapes(
keract.get_gradients_of_trainable_weights(model, x_train[0:10],
y_train[0:10]))
utils.print_names_and_shapes(
keract.get_gradients_of_activations(model, x_train[0:10],
y_train[0:10]))
a = keract.get_activations(model, x_test[0:1]) # with just one sample.
keract.display_activations(a, directory='mnist_activations', save=True)
# import numpy as np
# import matplotlib.pyplot as plt
# plt.imshow(np.squeeze(x_test[0:1]), interpolation='None', cmap='gray')
else:
x_train, y_train, x_test, y_test = MNIST.get_mnist_data()