I previously was able to run the search method of keras tuner on my model with GPU runtime of Google colab. But when I switched to the TPU runtime, I get the following error. I haven't been able to come to the conclusion of how to access a google cloud storage for the TPU runtime to save the checkpoint folder that the keras tuner saves model checkpoints in. I also don't know how to do it and I'm getting the following error. Please help me resolve this issue.
My code:
def post_se(hp):
ip = Input(shape=(6, 128))
x = Masking()(ip)
x = LSTM(units=hp.Choice('lstm_1', values = [8,16,32,64,128,256,512]),return_sequences=True)(x)
x = Dropout(hp.Choice(name='Dropout', values = [0.0,0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]))(x)
x = LSTM(units=hp.Choice('lstm_2', values = [8,16,32,64,128,256,512]))(x)
x = Dropout(hp.Choice(name='Dropout_2', values = [0.0,0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]))(x)
y = Permute((2, 1))(ip)
y = Conv1D(hp.Choice('conv_1_filter', values = [32,64,128,256,512]), hp.Choice(name='conv_1_filter_size', values = [3,5,7,8,9]), padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = squeeze_excite_block(y)
y = Conv1D(hp.Choice('conv_2_filter', values = [32,64,128,256,512]), hp.Choice(name='conv_2_filter_size',values = [3,5,7,8,9]), padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = squeeze_excite_block(y)
y = Conv1D(hp.Choice('conv_3_filter', values = [32,64,128,256,512,]), hp.Choice(name='conv_3_filter_size',values = [3,5,7,8,9]), padding='same', kernel_initializer='he_uniform')(y)
y = BatchNormalization()(y)
y = Activation('relu')(y)
y = GlobalAveragePooling1D()(y)
x = concatenate([x,y])
# batch_size = hp.Choice('batch_size', values=[32, 64, 128, 256, 512, 1024, 2048, 4096])
out = Dense(num_classes, activation='softmax')(x)
model = Model(ip, out)
if gpu:
opt = keras.optimizers.Adam(learning_rate=0.001)
if tpu:
opt = keras.optimizers.Adam(learning_rate=8*0.001)
model.compile(optimizer=opt, loss='categorical_crossentropy',metrics=['accuracy'])
# model.summary()
return model
if gpu:
tuner = kt.tuners.BayesianOptimization(post_se,
objective='val_accuracy',
max_trials=30,
seed=42,
project_name='Model_gpu')
# Will stop training if the "val_loss" hasn't improved in 30 epochs.
tuner.search(X_train, train_label, epochs=200, validation_split=0.1, shuffle=True, callbacks=[tensorflow.keras.callbacks.EarlyStopping('val_loss', patience=30)])
if tpu:
print("TPU")
with strategy.scope():
tuner = kt.tuners.BayesianOptimization(post_se,
objective='val_accuracy',
max_trials=30,
seed=42,
project_name='Model_tpu')
# Will stop training if the "val_loss" hasn't improved in 30 epochs.
tuner.search(X_train, train_label, epochs=200, validation_split=0.1, shuffle=True, callbacks=[tensorflow.keras.callbacks.EarlyStopping('val_loss', patience=30)])
The error log
---------------------------------------------------------------------------
UnimplementedError Traceback (most recent call last)
/usr/lib/python3.7/contextlib.py in __exit__(self, type, value, traceback)
129 try:
--> 130 self.gen.throw(type, value, traceback)
131 except StopIteration as exc:
10 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py in resource_creator_scope(resource_type, resource_creator)
2957 resource_creator):
-> 2958 yield
2959
<ipython-input-15-24c1e1bb603d> in <module>()
17 # Will stop training if the "val_loss" hasn't improved in 30 epochs.
---> 18 tuner.search(X_train, train_label, epochs=200, validation_split=0.1, shuffle=True, callbacks=[tensorflow.keras.callbacks.EarlyStopping('val_loss', patience=30)])
/usr/local/lib/python3.7/dist-packages/keras_tuner/engine/base_tuner.py in search(self, *fit_args, **fit_kwargs)
178 self.on_trial_begin(trial)
--> 179 results = self.run_trial(trial, *fit_args, **fit_kwargs)
180 # `results` is None indicates user updated oracle in `run_trial()`.
/usr/local/lib/python3.7/dist-packages/keras_tuner/engine/tuner.py in run_trial(self, trial, *args, **kwargs)
303 copied_kwargs["callbacks"] = callbacks
--> 304 obj_value = self._build_and_fit_model(trial, *args, **copied_kwargs)
305
/usr/local/lib/python3.7/dist-packages/keras_tuner/engine/tuner.py in _build_and_fit_model(self, trial, *args, **kwargs)
233 model = self._try_build(hp)
--> 234 return self.hypermodel.fit(hp, model, *args, **kwargs)
235
/usr/local/lib/python3.7/dist-packages/keras_tuner/engine/hypermodel.py in fit(self, hp, model, *args, **kwargs)
136 """
--> 137 return model.fit(*args, **kwargs)
138
/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py in _numpy(self)
1116 except core._NotOkStatusException as e: # pylint: disable=protected-access
-> 1117 raise core._status_to_exception(e) from None # pylint: disable=protected-access
1118
UnimplementedError: File system scheme '[local]' not implemented (file: './untitled_project/trial_78ed6883514d67dc6222064095c134cb/checkpoints/epoch_0/checkpoint_temp/part-00000-of-00001')
Encountered when executing an operation using EagerExecutor. This error cancels all future operations and poisons their output tensors.
During handling of the above exception, another exception occurred:
IndexError Traceback (most recent call last)
<ipython-input-15-24c1e1bb603d> in <module>()
16 seed=42)
17 # Will stop training if the "val_loss" hasn't improved in 30 epochs.
---> 18 tuner.search(X_train, train_label, epochs=200, validation_split=0.1, shuffle=True, callbacks=[tensorflow.keras.callbacks.EarlyStopping('val_loss', patience=30)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py in __exit__(self, exception_type, exception_value, traceback)
454 "tf.distribute.set_strategy() out of `with` scope."),
455 e)
--> 456 _pop_per_thread_mode()
457
458
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribution_strategy_context.py in _pop_per_thread_mode()
64
65 def _pop_per_thread_mode():
---> 66 ops.get_default_graph()._distribution_strategy_stack.pop(-1) # pylint: disable=protected-access
67
68
IndexError: pop from empty list
For some extra info, I am attaching my code in this post.
This is your error:
UnimplementedError: File system scheme '[local]' not implemented (file: './untitled_project/trial_78ed6883514d67dc6222064095c134cb/checkpoints/epoch_0/checkpoint_temp/part-00000-of-00001')
See https://stackoverflow.com/a/62881833/14043558 for a solution.
Related
I'm trying to load a Keras sequential model and get some error of unexpected keyword reduction.
Here's the code:
I've trained the following Sequential model:
model = Sequential([
tf.keras.Input(shape=(in_dim,)),
layers.Dense(
units=(in_dim+1),
activation=layers.LeakyReLU(alpha=.01)
),
layers.Dropout(rate=.05),
layers.Dense(
units=(in_dim),
activation=layers.LeakyReLU(alpha=.01)
),
layers.Dropout(rate=.05),
layers.Dense(units=1, activation="sigmoid")
])
and compile and fit it by Keras's tutorial:
def compile_and_fit(
model,
name,
X_y_train,
X_y_val,
optimizer=None,
max_epochs=10000,
batch_size=BATCH_SIZE,
):
X_train, y_train = X_y_train
steps_per_epoch = len(X_train) // batch_size
X_val, y_val = X_y_val
steps_per_epoch_val = len(X_val) // batch_size
if optimizer is None:
optimizer = get_optimizer(steps_per_epoch)
model.compile(
optimizer=optimizer,
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=[
tf.keras.losses.BinaryCrossentropy(name="binary_crossentropy"),
tf.keras.metrics.Precision(name="precision"),
"accuracy",
],
)
model.summary()
history = model.fit(
x=X_train,
y=y_train,
steps_per_epoch=steps_per_epoch,
batch_size=batch_size,
epochs=max_epochs,
validation_data=X_y_val,
validation_steps=steps_per_epoch_val,
callbacks=get_callbacks(name),
verbose=1,
)
return history
And at the end saving the model:
model.save(f"./saved_model/my_model", save_format="tf")
But When I tried to load it:
model = tf.keras.models.load_model("./saved_model/my_model")
And got the following error:
TypeError Traceback (most recent call last)
Cell In [25], line 2
----> 2 model = tf.keras.models.load_model("./saved_model/my_model")
File /mnt/c/Code/venv/lib/python3.9/site-packages/keras/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File /mnt/c/Code/venv/lib/python3.9/site-packages/keras/dtensor/utils.py:144, in inject_mesh.<locals>._wrap_function(instance, *args, **kwargs)
142 if mesh is not None:
143 instance._mesh = mesh
--> 144 init_method(instance, *args, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'reduction'
Any ideas on how to fix it?
mixed_precision.set_global_policy(policy="mixed_float16") gives an error when I add this line
error =
TypeError Traceback (most recent call
last) in
5 #mixed_precision.set_global_policy(policy="float32")
6 input_shape = (224, 224, 3)
----> 7 base_model = tf.keras.applications.EfficientNetB0(include_top=False)
8 base_model.trainable = False # freeze base model layers
9
4 frames
/usr/local/lib/python3.7/dist-packages/keras/applications/efficientnet.py
in EfficientNetB0(include_top, weights, input_tensor, input_shape,
pooling, classes, classifier_activation, **kwargs)
559 classes=classes,
560 classifier_activation=classifier_activation,
--> 561 **kwargs)
562
563
/usr/local/lib/python3.7/dist-packages/keras/applications/efficientnet.py
in EfficientNet(width_coefficient, depth_coefficient, default_size,
dropout_rate, drop_connect_rate, depth_divisor, activation,
blocks_args, model_name, include_top, weights, input_tensor,
input_shape, pooling, classes, classifier_activation)
332 # original implementation.
333 # See https://github.com/tensorflow/tensorflow/issues/49930 for more details
--> 334 x = x / tf.math.sqrt(IMAGENET_STDDEV_RGB)
335
336 x = layers.ZeroPadding2D(
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py
in error_handler(*args, **kwargs)
151 except Exception as e:
152 filtered_tb = _process_traceback_frames(e.traceback)
--> 153 raise e.with_traceback(filtered_tb) from None
154 finally:
155 del filtered_tb
/usr/local/lib/python3.7/dist-packages/keras/layers/core/tf_op_layer.py
in handle(self, op, args, kwargs)
105 isinstance(x, keras_tensor.KerasTensor)
106 for x in tf.nest.flatten([args, kwargs])):
--> 107 return TFOpLambda(op)(*args, **kwargs)
108 else:
109 return self.NOT_SUPPORTED
/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py
in error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.traceback)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
TypeError: Exception encountered when calling layer
"tf.math.truediv_3" (type TFOpLambda).
x and y must have the same dtype, got tf.float16 != tf.float32.
Call arguments received by layer "tf.math.truediv_3" (type
TFOpLambda): • x=tf.Tensor(shape=(None, None, None, 3),
dtype=float16) • y=tf.Tensor(shape=(3,), dtype=float32) •
name=None
this is code =
from tensorflow.keras import layers
# Create base model
mixed_precision.set_global_policy(policy="mixed_float16")
input_shape = (224, 224, 3)
base_model = tf.keras.applications.EfficientNetB0(include_top=False)
base_model.trainable = False # freeze base model layers
# Create Functional model
inputs = layers.Input(shape=input_shape, name="input_layer")
# Note: EfficientNetBX models have rescaling built-in but if your model didn't you could have a layer like below
# x = layers.Rescaling(1./255)(x)
x = base_model(inputs, training=False) # set base_model to inference mode only
x = layers.GlobalAveragePooling2D(name="pooling_layer")(x)
x = layers.Dense(len(class_names))(x) # want one output neuron per class
# Separate activation of output layer so we can output float32 activations
outputs = layers.Activation("softmax", dtype=tf.float32, name="softmax_float32")(x)
model = tf.keras.Model(inputs, outputs)
# Compile the model
model.compile(loss="sparse_categorical_crossentropy", # Use sparse_categorical_crossentropy when labels are *not* one-hot
optimizer=tf.keras.optimizers.Adam(),
metrics=["accuracy"])
When I change this line with float32 instead of mixed_float16,like
this mixed_precision.set_global_policy(policy="float32") the
error goes away. I want to use Mixed_precision, how can I do it?
I'm trying to run a baseline model which can be found here: https://github.com/gniknoil/FG2020-kinship/tree/master/Track1
here is the code:
def baseline_model():
input_1 = Input(shape=(224, 224, 3))
input_2 = Input(shape=(224, 224, 3))
base_model = VGGFace(model='resnet50', include_top=False)
for x in base_model.layers[:-3]:
x.trainable = True
x1 = base_model(input_1)
x2 = base_model(input_2)
x1=GlobalMaxPool2D()(x1)
x2=GlobalAvgPool2D()(x2)
x3 = Subtract()([x1, x2])
x3 = Multiply()([x3, x3])
x1_ = Multiply()([x1, x1])
x2_ = Multiply()([x2, x2])
x4 = Subtract()([x1_, x2_])
x5 = Multiply()([x1, x2])
x = Concatenate(axis=-1)([x3, x4, x5])
# x = Dense(512, activation="relu")(x)
# x = Dropout(0.03)(x)
x = Dense(128, activation="relu")(x)
x = Dropout(0.02)(x)
out = Dense(1, activation="sigmoid")(x)
model = Model([input_1, input_2], out)
model.compile(loss="binary_crossentropy", metrics=['acc'], optimizer=adamv2.Adam(0.00001))
#model.compile(loss=[focal_loss(alpha=.25, gamma=2)], metrics=['acc'], optimizer=Adam(0.00003))
#model.compile(loss=[focal_loss(alpha=.25, gamma=2)], metrics=['acc'], optimizer=Adam(0.00001))
model.summary()
return model
I got the error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/var/folders/1x/xq_p1_3x2wj0drhvr109gkfh0000gn/T/ipykernel_7112/1473799896.py in <module>
----> 1 model = baseline_model()
/var/folders/1x/xq_p1_3x2wj0drhvr109gkfh0000gn/T/ipykernel_7112/726929419.py in baseline_model()
31
32 def baseline_model():
---> 33 input_1 = Input(shape=(224, 224, 3))
34 input_2 = Input(shape=(224, 224, 3))
35
~/opt/anaconda3/envs/env-kinship/lib/python3.8/site-packages/keras/engine/input_layer.py in Input(shape, batch_shape, name, dtype, sparse, tensor)
173 if not dtype:
174 dtype = K.floatx()
--> 175 input_layer = InputLayer(batch_input_shape=batch_shape,
176 name=name, dtype=dtype,
177 sparse=sparse,
~/opt/anaconda3/envs/env-kinship/lib/python3.8/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~/opt/anaconda3/envs/env-kinship/lib/python3.8/site-packages/keras/engine/input_layer.py in __init__(self, input_shape, batch_size, batch_input_shape, dtype, input_tensor, sparse, name)
37 if not name:
38 prefix = 'input'
---> 39 name = prefix + '_' + str(K.get_uid(prefix))
40 super(InputLayer, self).__init__(dtype=dtype, name=name)
41
~/opt/anaconda3/envs/env-kinship/lib/python3.8/site-packages/keras/backend/tensorflow_backend.py in get_uid(prefix)
72 """
73 global _GRAPH_UID_DICTS
---> 74 graph = tf.get_default_graph()
75 if graph not in _GRAPH_UID_DICTS:
76 _GRAPH_UID_DICTS[graph] = defaultdict(int)
~/opt/anaconda3/envs/env-kinship/lib/python3.8/site-packages/tensorflow/python/util/deprecation_wrapper.py in __getattr__(self, name)
115 call_location = _call_location()
116 if not call_location.startswith('<'): # skip locations in Python source
--> 117 logging.warning(
118 'From %s: The name %s is deprecated. Please use %s instead.\n',
119 _call_location(), full_name, rename)
~/opt/anaconda3/envs/env-kinship/lib/python3.8/site-packages/tensorflow/python/platform/tf_logging.py in warning(msg, *args, **kwargs)
164 #tf_export(v1=["logging.error"])
165 def error(msg, *args, **kwargs):
--> 166 get_logger().error(msg, *args, **kwargs)
167
168
~/opt/anaconda3/envs/env-kinship/lib/python3.8/logging/__init__.py in warning(self, msg, *args, **kwargs)
1456 """
1457 if self.isEnabledFor(WARNING):
-> 1458 self._log(WARNING, msg, args, **kwargs)
1459
1460 def warn(self, msg, *args, **kwargs):
~/opt/anaconda3/envs/env-kinship/lib/python3.8/logging/__init__.py in _log(self, level, msg, args, exc_info, extra, stack_info, stacklevel)
1575 #IronPython can use logging.
1576 try:
-> 1577 fn, lno, func, sinfo = self.findCaller(stack_info, stacklevel)
1578 except ValueError: # pragma: no cover
1579 fn, lno, func = "(unknown file)", 0, "(unknown function)"
TypeError: _logger_find_caller() takes from 0 to 1 positional arguments but 2 were given
How can I fix this?
My Keras version is 2.2.4
My tensor flow version is 1.14.0
I'm using python 3.8.1 and an anaconda environment.
And I have tried the solution here: https://medium.com/the-rising-tilde/typeerror-logger-find-caller-takes-from-0-to-1-positional-arguments-but-2-were-given-cb24b74a6125
which doesn't work
You are using Tensorflow 1.14 with python 3.8 which is not compatible as mentioned in this doc.
Please upgrade the Tensorflow version as per installed python version 3.8 in your system which will be Tensorflow >= 2.2 and try again executing the same code.
!pip install --upgrade tensorflow
I want for training a CNN with Early Stopping and want to use the f1-metric as stopping criterion.
When I compile the code for the CNN model I get the a TypeError as error message.
I'm still using Tensorflow 1.4 would like to avoid an upgrade to 2.0, because I have in mind that my previous code doesn't work anymore.
The error message is as follows:
TypeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in _num_samples(x)
158 try:
--> 159 return len(x)
160 except TypeError:
14 frames
/usr/local/lib/python3.6/dist-
packages/tensorflow_core/python/framework/ops.py in __len__(self)
740 "Please call `x.shape` rather than `len(x)` for "
--> 741 "shape information.".format(self.name))
742
TypeError: len is not well defined for symbolic Tensors. (dense_16_target:0) Please call `x.shape` rather than `len(x)` for shape information.
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-44-cd3da16e057c> in <module>()
----> 1 model = model_cnn(False,False, False,True,6, 0.2, 0.5)
2 X_train, X_val, y_train, y_val = split_data(X_train, y_train,1)
3 cnn, ep = train_model_es(model, X_train, y_train, X_val, y_val, X_test, y_test, 50, 500,1)
<ipython-input-42-d275d9c69c03> in model_cnn(spat, extra_pool, avg_pool, cw, numb_conv, drop_conv, drop_dense)
36 if cw == True:
37 print("sparse categorical crossentropy")
---> 38 model.compile(loss="sparse_categorical_crossentropy", optimizer=Adam(), metrics=['accuracy', f1_metric])
39 #model.compile(loss="sparse_categorical_crossentropy", optimizer=Adam(), metrics=['accuracy'])
40 print("nothing")
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, **kwargs)
452 output_metrics = nested_metrics[i]
453 output_weighted_metrics = nested_weighted_metrics[i]
--> 454 handle_metrics(output_metrics)
455 handle_metrics(output_weighted_metrics, weights=weights)
456
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in handle_metrics(metrics, weights)
421 metric_result = weighted_metric_fn(y_true, y_pred,
422 weights=weights,
--> 423 mask=masks[i])
424
425 # Append to self.metrics_names, self.metric_tensors,
/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in weighted(y_true, y_pred, weights, mask)
426 """
427 # score_array has ndim >= 2
--> 428 score_array = fn(y_true, y_pred)
429 if mask is not None:
430 # Cast the mask to floatX to avoid float64 upcasting in Theano
<ipython-input-9-b21dc3bd89a6> in f1_metric(y_test, y_pred)
1 def f1_metric(y_test, y_pred):
----> 2 f1 = f1_score(y_test, y_pred, average='macro')
3 return f1
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)
1097 pos_label=pos_label, average=average,
1098 sample_weight=sample_weight,
-> 1099 zero_division=zero_division)
1100
1101
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)
1224 warn_for=('f-score',),
1225 sample_weight=sample_weight,
-> 1226 zero_division=zero_division)
1227 return f
1228
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)
1482 raise ValueError("beta should be >=0 in the F-beta score")
1483 labels = _check_set_wise_labels(y_true, y_pred, average, labels,
-> 1484 pos_label)
1485
1486 # Calculate tp_sum, pred_sum, true_sum ###
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
1299 str(average_options))
1300
-> 1301 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
1302 present_labels = unique_labels(y_true, y_pred)
1303 if average == 'binary':
/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in _check_targets(y_true, y_pred)
78 y_pred : array or indicator matrix
79 """
---> 80 check_consistent_length(y_true, y_pred)
81 type_true = type_of_target(y_true)
82 type_pred = type_of_target(y_pred)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
206 """
207
--> 208 lengths = [_num_samples(X) for X in arrays if X is not None]
209 uniques = np.unique(lengths)
210 if len(uniques) > 1:
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in <listcomp>(.0)
206 """
207
--> 208 lengths = [_num_samples(X) for X in arrays if X is not None]
209 uniques = np.unique(lengths)
210 if len(uniques) > 1:
/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in _num_samples(x)
159 return len(x)
160 except TypeError:
--> 161 raise TypeError(message)
162
163
TypeError: Expected sequence or array-like, got <class 'tensorflow.python.framework.ops.Tensor'>
And here is the relevant code:
def f1_metric(y_test, y_pred):
f1 = f1_score(y_test, y_pred, average='macro')
return f1
def train_model_es(model, X, y, X_val, y_val, X_test, y_test):
es = EarlyStopping(monitor='f1_metric', mode='max', patience=20, restore_best_weights=True)
y = np.argmax(y, axis=1)
y_val = np.argmax(y_val, axis=1)
y_test = np.argmax(y_test, axis=1)
class_weights = class_weight.compute_class_weight('balanced', np.unique(y), y)
class_weights = dict(enumerate(class_weights))
history = model.fit(X, y, class_weight=class_weights, batch_size=32,epochs=20, verbose=1,
validation_data=(X_val, y_val), callbacks=[es])
def model_cnn():
model = Sequential()
model.add(Conv2D(32, kernel_size=(3,3), input_shape=(28,28,1),padding='same'))
model.add(BatchNormalization())
model.add(ELU())
model.add(Conv2D(32, kernel_size=(3,3), padding='same'))
model.add(BatchNormalization())
model.add(ELU())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(256))
model.add(BatchNormalization())
model.add(ELU())
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(loss="sparse_categorical_crossentroy",optimizer=Adam(),metrics=["accuracy",f1_metric])
return model
Does anyone have a tip on how to fix this error message.
Many thanks for every hint
As the error message suggests,
Error 1) You are doing a len() operation on symbolic tensor. You cannot do that operation on symbolic tensor. You can find difference between a variable tensor and symbolic tensor here.
Error 2) You are using a tensor for the operation which expects array as input. Can you please convert y_true and y_pred from tensor to array and use in the f1_score and other operations.
Example - To convert tensor to array
%tensorflow_version 1.x
print(tf.__version__)
import tensorflow as tf
import numpy as np
x = tf.constant([1,2,3,4,5,6])
print("Type of x:",x)
with tf.Session() as sess:
y = np.array(x.eval())
print("Type of y:",y.shape,y)
Output -
1.15.2
Type of x: Tensor("Const_24:0", shape=(6,), dtype=int32)
Type of y: (6,) [1 2 3 4 5 6]
I'm going over the Book Deep Learning with Python from F. Chollet.
https://www.manning.com/books/deep-learning-with-python
I'm trying to follow along with the code examples. I just installed keras, and I am getting this error when trying to run this:
from this notebook:
https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/2.1-a-first-look-at-a-neural-network.ipynb
from keras import models
from keras import layers
network = models.Sequential()
network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))
network.add(layers.Dense(10, activation='softmax'))
TypeError Traceback (most recent call
last) in ()
4 network = models.Sequential()
5 network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))
----> 6 network.add(layers.Dense(10, activation='softmax'))
~/anaconda3/lib/python3.6/site-packages/keras/engine/sequential.py in
add(self, layer)
179 self.inputs = network.get_source_inputs(self.outputs[0])
180 elif self.outputs:
--> 181 output_tensor = layer(self.outputs[0])
182 if isinstance(output_tensor, list):
183 raise TypeError('All layers in a Sequential model '
~/anaconda3/lib/python3.6/site-packages/keras/engine/base_layer.py in
call(self, inputs, **kwargs)
455 # Actually call the layer,
456 # collecting output(s), mask(s), and shape(s).
--> 457 output = self.call(inputs, **kwargs)
458 output_mask = self.compute_mask(inputs, previous_mask)
459
~/anaconda3/lib/python3.6/site-packages/keras/layers/core.py in
call(self, inputs)
881 output = K.bias_add(output, self.bias, data_format='channels_last')
882 if self.activation is not None:
--> 883 output = self.activation(output)
884 return output
885
~/anaconda3/lib/python3.6/site-packages/keras/activations.py in
softmax(x, axis)
29 raise ValueError('Cannot apply softmax to a tensor that is 1D')
30 elif ndim == 2:
---> 31 return K.softmax(x)
32 elif ndim > 2:
33 e = K.exp(x - K.max(x, axis=axis, keepdims=True))
~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py
in softmax(x, axis) 3229 A tensor. 3230 """
-> 3231 return tf.nn.softmax(x, axis=axis) 3232 3233
TypeError: softmax() got an unexpected keyword argument 'axis'
I'm wondering if there's something off with my installation?
keras.__version__
2.2.4
If anyone could give me a clue of what to look into.
Seems like you have an incompatible Tensorflow version (which Keras is using as a backend). For details look here