Create a simple lstm net for testing Keras model import. Run
Keras simple_lstm.py example and then save that model and its
outputs to disk.
'''
from __future__ import print_function
import imp
import keras.backend as K
from util import save_model_details, save_model_output
SCRIPT_PATH = '../examples/simple_lstm.py'
KERAS_VERSION = '_keras_2'
PREFIX = 'simple_lstm_' + K.image_dim_ordering() + KERAS_VERSION
OUT_DIR = '.'
print('Entering Keras script')
example = imp.load_source('example', SCRIPT_PATH)
print('Saving model details')
save_model_details(example.model, prefix=PREFIX, out_dir=OUT_DIR)
print('Saving model outputs')
save_model_output(example.model,
example.cos,
example.expected_output,
nb_examples=100,
prefix=PREFIX,
out_dir=OUT_DIR)
print('DONE!')
Create a simple MLP for testing Keras model import. Run
Keras mnist_mlp_constraints.py example and then save that model and its
outputs to disk.
'''
from __future__ import print_function
import imp
import keras.backend as K
from util import save_model_details, save_model_output
SCRIPT_PATH = '../examples/mnist_mlp_constraints.py'
KERAS_VERSION = '_keras_2'
PREFIX = 'mnist_mlp_' + K.image_dim_ordering() + KERAS_VERSION
OUT_DIR = '.'
print('Entering Keras script')
example = imp.load_source('example', SCRIPT_PATH)
print('Saving model details')
save_model_details(example.model, prefix=PREFIX, out_dir=OUT_DIR)
print('Saving model outputs')
save_model_output(example.model,
example.X_test,
example.Y_test,
nb_examples=100,
prefix=PREFIX,
out_dir=OUT_DIR)
print('DONE!')
input_shape=input_shape))
#model.add(Conv1D(name="conv1", filters=3, kernel_size=k, strides=s, padding="causal", data_format=f, dilation_rate=d, activation=None, use_bias=False))
#model.add(GlobalAveragePooling1D(data_format=f))
#model.add(Activation('softmax'))
opt = keras.optimizers.RMSprop(learning_rate=0.0001,
decay=1e-6)
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
print('Saving model details')
save_model_details(model, prefix=name, out_dir=OUT_DIR)
exp_out = model.predict(features)
print("Input = ", str(features.shape), ", Out = ",
str(exp_out.shape), " case - ", name)
print('Saving model outputs')
save_model_output(model,
features,
exp_out,
nb_examples=None,
prefix=name,
out_dir=OUT_DIR,
labels=None) #labels)
print('DONE!')
