Created
September 5, 2018 12:17
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Use TensorBoard's runtime statistcs with a Keras model
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| import numpy as np | |
| import tensorflow as tf | |
| sess = tf.Session() | |
| from keras import backend as K | |
| K.set_session(sess) | |
| from keras.objectives import mean_squared_error | |
| from keras.layers import Dense | |
| def load_dummy_data(n = 1000): | |
| x = np.random.rand(n, 100) | |
| y = np.sum(x, axis=1, keepdims=True) | |
| return x, y | |
| def load_keras_network(input_tensor): | |
| x = Dense(100, activation='relu')(input_tensor) | |
| x = Dense(100, activation='relu')(x) | |
| x = Dense(100, activation='relu')(x) | |
| x = Dense(1, activation='sigmoid')(x) | |
| return x | |
| # load your keras model as a tf.Tensor | |
| input = tf.placeholder(tf.float32, shape=(None, 100)) # is passed as input to our keras layers | |
| labels = tf.placeholder(tf.float32, shape=(None, 1)) | |
| net = load_network(input) # type(net) == tf.Tensor | |
| loss = tf.reduce_mean(mean_squared_error(labels, net)) | |
| opt = tf.train.GradientDescentOptimizer(0.1).minimize(loss) | |
| writer = tf.summary.FileWriter(r'./logs', sess.graph) | |
| sess.run(tf.global_variables_initializer()) | |
| with sess.as_default(): | |
| x, y = load_data(64) | |
| run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) | |
| run_metadata = tf.RunMetadata() | |
| sess.run([opt], | |
| feed_dict={input: x, labels: y}, | |
| options=run_options, | |
| run_metadata=run_metadata) | |
| writer.add_run_metadata(run_metadata, 'runtime-stats') | |
| writer.close() |
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