as a continuation to Tensorflow - ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float)
I had a similar issue where I had the following error
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray).
I followed the different suggestions but it does not seem to solve my problem.
all the values below are <class 'numpy.ndarray'>
train_inputs=df_train_title_train
train_targets=y_train.to_numpy()
validation_inputs=df_train_title_test
validation_targets=y_test.to_numpy()
shapes are (63586,), (63586, 9), (7066,), (7066, 9) respectively where 9 is the number of class I am trying to classify
# Set the input and output sizes
input_size = 64
output_size = 9
# Use same hidden layer size for both hidden layers. Not a necessity.
hidden_layer_size = 64
# define how the model will look like
model = tf.keras.Sequential([
tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 1st hidden layer
tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer
tf.keras.layers.Dense(hidden_layer_size, activation='relu'), # 2nd hidden layer
tf.keras.layers.Dense(output_size, activation='softmax') # output layer
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
### Training
# That's where we train the model we have built.
# set the batch size
batch_size = 10
# set a maximum number of training epochs
max_epochs = 10
# fit the model
# note that this time the train, validation and test data are not iterable
model.fit(train_inputs, # train inputs
train_targets, # train targets
batch_size=batch_size, # batch size
epochs=max_epochs, # epochs that we will train for (assuming early stopping doesn't kick in)
validation_data=(validation_inputs, validation_targets), # validation data
verbose = 2 # making sure we get enough information about the training process
)
Finally the error looks like this.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-75-10183099f9ec> in <module>()
6 epochs=max_epochs, # epochs that we will train for (assuming early stopping doesn't kick in)
7 validation_data=(validation_inputs, validation_targets), # validation data
----> 8 verbose = 2 # making sure we get enough information about the training process
9 )
16 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
96 dtype = dtypes.as_dtype(dtype).as_datatype_enum
97 ctx.ensure_initialized()
---> 98 return ops.EagerTensor(value, ctx.device_name, dtype)
99
100
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray).
