import numpy as np

from tensorflow.python.keras import models
from tensorflow.python.keras import layers
from tensorflow.python.keras import backend as K

inp = layers.Input(shape=(10, 20,))
conv = layers.Conv1D(filters=10, kernel_size=2)(inp)
pool = layers.GlobalMaxPool1D()(conv)
output = layers.Dense(1, activation="sigmoid")(pool)

m = models.Model(inp, output)

m.summary()

m.compile(optimizer="adam", loss="binary_crossentropy")

m.fit(x=np.random.randn(100, 10, 20), y=np.random.randn(100))

loss = K.mean(m.output)
grads = K.gradients(loss, m.input)[0]
f = K.function([m.input], [grads])
print(f([np.random.randn(10, 20)]))

import tensorflow as tf
import sys
from tensorflow.python import keras

print(tf.__version__)
print(keras.__version__)
print(sys.version)

1.12.0
2.1.6-tf
3.6.7 |Anaconda, Inc.| (default, Oct 23 2018, 14:01:38)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]

2019-04-19 17:00:58.249788: F ./tensorflow/core/util/tensor_format.h:420] Check failed: index >= 0 && index < dimension_attributes.size() Invalid index from the dimension: 3, 0, C