from tensorflow import keras
from tensorflow.python.keras.constraints import max_norm
import tensorflow as tf
import numpy as np
optimizer_map = {
'sgd': tf.train.GradientDescentOptimizer,
'adam': tf.train.AdamOptimizer,
}
activation_fn_map = {
'tanh': tf.nn.tanh,
'relu': tf.nn.relu,
'sigmoid': tf.nn.sigmoid,
'softsign': tf.nn.softsign,
'softplus': tf.nn.softplus,
'leaky_relu': tf.nn.leaky_relu,
'hard_sigmoid': 'hard_sigmoid',
'selu': tf.nn.selu,
'relu6': tf.nn.relu6,
'elu': tf.nn.elu,
'linear': 'linear'
}
[docs]def load(architecture, activation_fn, optimizer, learning_rate, input_size, output_size, max_norm_weights=False):
input_layer = keras.layers.Input((input_size,))
clayer = input_layer
for n in architecture:
clayer = keras.layers.Dense(n,
activation=activation_fn_map[activation_fn],
kernel_initializer=keras.initializers.TruncatedNormal(mean=0.0,
stddev=1 / np.sqrt(float(n)),
seed=None),
bias_initializer='zeros',
kernel_constraint=(max_norm(max_value=float(max_norm_weights))
if max_norm_weights else None)
)(clayer)
clayer = keras.layers.BatchNormalization()(clayer)
output_layer = keras.layers.Dense(output_size, activation='softmax')(clayer)
model = keras.models.Model(inputs=input_layer, outputs=output_layer)
optimizer = optimizer_map[optimizer](learning_rate=learning_rate)
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
return model