Comments (2)
The plot thickens ... I was curious so added model.set_weights(model.get_weights())
just before the call to model.evaluate()
, hoping that would knock something loose in the model's internal state and fix things. Instead, it actually made it so that model inference resulted in garbage regardless of the value of restore_best_weights
. This makes reproducing this issue even simpler.
import numpy as np
import tensorflow as tf
X = np.tile(np.arange(10), reps=100)
y = 1.5 * X - 1
print("tf:", tf.__version__)
model = tf.keras.models.Sequential([tf.keras.layers.Input((1, )), tf.keras.layers.Dense(1)])
model.compile(loss="mse", optimizer="rmsprop")
model.fit(X, y, epochs=200, verbose=0)
print("before set_weights, mse:", model.evaluate(X, y), "weights:", [a.flatten()[0] for a in model.get_weights()])
model.set_weights(model.get_weights())
print("after set_weights, mse:", model.evaluate(X, y), "weights:", [a.flatten()[0] for a in model.get_weights()])
Output using tensorflow_macos
...
tf: 2.4.0-rc0
32/32 [==============================] - 0s 215us/step - loss: 1.8945e-06
before set_weights, mse: 1.8945354440802475e-06 weights: [1.4997802, -1.000234]
32/32 [==============================] - 0s 231us/step - loss: inf
after set_weights, mse: inf weights: [1.4997802, -1.000234]
Output using Google Colab ...
tf: 2.4.0-rc0
32/32 [==============================] - 0s 874us/step - loss: 2.0693e-07
before set_weights, mse: 2.0693499891422107e-07 weights: [1.4999211, -1.0000395]
32/32 [==============================] - 0s 785us/step - loss: 2.0693e-07
after set_weights, mse: 2.0693499891422107e-07 weights: [1.4999211, -1.0000395]
So it seems that any use of model.set_weights()
(which is probably being used under the hood by restore_best_weights
) results in a broken model state.
from tensorflow_macos.
The issue can be avoided (albeit defeating the purpose of the fork) using:
import os
os.environ["TF_DISABLE_MLC"] = "1"
from tensorflow_macos.
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from tensorflow_macos.