I'm trying to run a basic CNN keras model in Eager Execution but Tensorflow refuses to treat the model as eager. I originally attempted this in stable 1.13 branch (latest), making sure to enable eager execution with no result. I upgraded to 2.0 (latest) but again nothing.
class CNN2(tf.keras.Model):
def __init__(self, num_classes=7):
super(CNN2, self).__init__()
self.cnn1 = tf.keras.layers.Conv2D(32, (5,5), padding='same', strides=(2, 2),
kernel_initializer='he_normal')
self.bn1 = tf.keras.layers.BatchNormalization()
self.cnn2 = tf.keras.layers.Conv2D(64, (5,5), padding='same', strides=(2, 2),
kernel_initializer='he_normal')
self.cnn3 = tf.keras.layers.Conv2D(128, (5,5), padding='same', strides=(2, 2),
kernel_initializer='he_normal')
self.bn2 = tf.keras.layers.BatchNormalization()
self.pool = tf.keras.layers.MaxPooling2D((2,2))
self.dnn1 = tf.keras.layers.Dense(128)
self.dropout1 = tf.keras.layers.Dropout(0.45)
self.flatten = tf.keras.layers.Flatten()
self.dnn2 = tf.keras.layers.Dense(512)
self.dnn3 = tf.keras.layers.Dense(256)
self.classifier = tf.keras.layers.Dense(num_classes)
def simpleLoop(self, inputs, x):
#x_Numpy = x.numpy(),
for i, input in inputs:
print("{0} - {1}".format(i,len(input)))
def call(self, inputs, training=None, mask=None):
print(tf.executing_eagerly())
x = tf.nn.leaky_relu(self.cnn1(inputs))
x = self.bn1(x)
x = self.pool(x)
x = tf.nn.leaky_relu(x)
x = tf.nn.leaky_relu(self.bn2(self.cnn2(x)))
x = self.pool(x)
x = self.dropout1(tf.nn.leaky_relu(self.cnn3(x)))
x = self.flatten(x)
self.simpleLoop(inputs, x)
x = self.dropout1(self.dnn1(x))
x = self.dropout1(self.dnn2(x))
x = self.dropout1(self.dnn3(x))
output = self.classifier(x)
#with tf.device('/cpu:0'):
output = tf.nn.softmax(output)
return output
batch_size = 50
epochs = 150
num_classes = 7
print(tf.executing_eagerly())
print(tf.__version__)
>>True
>>2.0.0-alpha0
modelE = CNN2(num_classes)
modelE.run_eagerly = True
print(modelE.run_eagerly)
#model = CNN2(num_classes)
modelE.compile(optimizer=tf.optimizers.Adam(0.00008), loss='categorical_crossentropy',
metrics=['accuracy'], run_eagerly=True)
# TF Keras tries to use entire dataset to determine shape without this step when using .fit()
# Fix = Use exactly one sample from the provided input dataset to determine input/output shape/s for the model
dummy_x = tf.zeros((1, size, size, 1))
modelE._set_inputs(dummy_x)
# Train
hist = modelE.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,
validation_data=(x_test, y_test), verbose=1)
# Evaluate on test set
scores = modelE.evaluate(x_test, y_test, batch_size, verbose=1)
This results in the error
AttributeError: 'Tensor' object has no attribute 'numpy'
And when I remove the offending line x.numpy()
I instead get this error
TypeError: Tensor objects are only iterable when eager execution is enabled. To iterate over this tensor use tf.map_fn.
It also prints False for the print(tf.executing_eagerly())
located within the def call()
method of the model.
How can it be forced into eager mode and not a graph? Again I tried this in both up to date 1.13 and 2.0. Is this a bug?
It took a while to find a solution that works for me in tensorflow==2.0.0
, so I wanted to share it here in case it helps other people too:
model.compile(run_eagerly=True)
If that doesn't work, you can try to force it after the model compiles:
model.compile()
model.run_eagerly = True
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