On tf version 2.11.0.
I have tried to follow on a fairly standard NN example in order to convert to a CoreML model. However, I cannot get this to work and I'm not clear where it is going wrong. It would seem to be a fairly standard task - a toy example - and I can't see why the conversion would fail.
Any help would be appreciated. I have tried the different approaches listed below, but it seems the conversion should just work.
I have also tried running the same code pinned to: tensorflow==2.6.2 scikit-learn==0.19.2 pandas==1.1.1
And get a different sequence of errors.
The Python code I used mostly comes form this example: https://lnwatson.co.uk/posts/intro_to_nn/
import pandas as pd
import numpy as np
import tensorflow as tf
import torch
from sklearn.model_selection import train_test_split
from tensorflow import keras
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
np.bool = np.bool_
np.int = np.int_
print("tf version", tf.__version__)
csv_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'
col_names = ['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width','Class']
df = pd.read_csv(csv_url, names = col_names)
labels = df.pop('Class')
labels = pd.get_dummies(labels)
X = df.values
y = labels.values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)
model = keras.Sequential()
model.add(keras.layers.Dense(16, activation='relu', input_shape=(4,)))
model.add(keras.layers.Dense(3, activation='softmax'))
model.summary()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train,
batch_size=12,
epochs=200,
validation_data=(X_val, y_val))
import coremltools as ct
# Pass in `tf.keras.Model` to the Unified Conversion API
mlmodel = ct.convert(model, convert_to="mlprogram")
# mlmodel = ct.convert(model, source="tensorflow")
# mlmodel = ct.convert(model, convert_to="neuralnetwork")
# mlmodel = ct.convert(
# model,
# source="tensorflow",
# inputs=[ct.TensorType(name="input")],
# outputs=[ct.TensorType(name="output")],
# minimum_deployment_target=ct.target.iOS14,
# )
When using either of these 3:
mlmodel = ct.convert(model, convert_to="mlprogram")
mlmodel = ct.convert(model, source="tensorflow")
mlmodel = ct.convert(model, convert_to="neuralnetwork")
I get:
mlmodel2 = ct.convert(model, source="tensorflow")
ValueError: Const node 'sequential_5/dense_10/MatMul/ReadVariableOp' cannot have no value
ERROR:root:sequential_5/dense_11/BiasAdd/ReadVariableOp:0
ERROR:root:[ 0.34652767 0.16202268 -0.3554725 ]
Running TensorFlow Graph Passes: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 28.76 passes/s]
Converting Frontend ==> MIL Ops: 8%|█████████████████ | 1/12 [00:00<00:00, 16710.37 ops/s]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
File ~/Documents/CoreML Basic Models/NN_Keras_Iris.py:142
130 import coremltools as ct
131 # Pass in `tf.keras.Model` to the Unified Conversion API
132 # mlmodel = ct.convert(model, convert_to="mlprogram")
133
(...)
140
141 # ct.convert(mymodel(), source="tensorflow")
--> 142 mlmodel2 = ct.convert(model, source="tensorflow")
144 mlmodel = ct.convert(
145 model,
146 source="tensorflow",
(...)
153 minimum_deployment_target=ct.target.iOS14,
154 )
....
File ~/opt/anaconda3/envs/coreml_env/lib/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow/ops.py:430, in Const(context, node)
427 @register_tf_op
428 def Const(context, node):
429 if node.value is None:
--> 430 raise ValueError("Const node '{}' cannot have no value".format(node.name))
431 mode = get_const_mode(node.value.val)
432 x = mb.const(val=node.value.val, mode=mode, name=node.name)
ValueError: Const node 'sequential_5/dense_10/MatMul/ReadVariableOp' cannot have no value
Second Approach:
A different approach I tried was specifying the inout type TensorType.
However, when specifying the input and outputs I get a different error. I have tried variations on this initialiser but all produce the same error.
The variations revolve around adding input_shape, dtype=np.float32
mlmodel = ct.convert(
model,
source="tensorflow",
inputs=[ct.TensorType(name="input")],
outputs=[ct.TensorType(name="output")],
minimum_deployment_target=ct.target.iOS14,
)
t
File ~/opt/anaconda3/envs/coreml_env/lib/python3.8/site-packages/coremltools/converters/mil/frontend/tensorflow/load.py:106, in <listcomp>(.0)
104 logging.debug(msg.format(outputs))
105 outputs = outputs if isinstance(outputs, list) else [outputs]
--> 106 outputs = [i.split(":")[0] for i in outputs]
107 if _get_version(tf.__version__) < _StrictVersion("1.13.1"):
108 return tf.graph_util.extract_sub_graph(graph_def, outputs)
AttributeError: 'TensorType' object has no attribute 'split'
