I have this neural network based on this
words_input = Input(shape=(500,),dtype='int32',name='words_input')
words = Embedding(input_dim=wordEmbeddings.shape[0], output_dim=wordEmbeddings.shape[1], weights=[wordEmbeddings], trainable=False)(words_input)
conv_1 = Conv1D(filters=100, kernel_size=10, strides=2, activation='relu')(words)
avgpool_1 = AveragePooling1D(pool_size=10, strides=10)(conv_1)
b_lstm = Bidirectional(LSTM(200, activation='tanh', return_sequences=False))(avgpool_1)
dense_1 = Dense(128, activation='relu')(b_lstm)
dropout = Dropout(0.1)(dense_1)
dense_2 = Dense(5, activation='softmax')(dropout)
sgd = keras.optimizers.Adam(lr=0.0001)
model = Model(inputs=words_input, outputs=dense_2)
extractor = Model(inputs=model.inputs, outputs=model.get_layer(words).output)
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['acc'])
model.summary()
I added the line
extractor = Model(inputs=model.inputs, outputs=model.get_layer(words).output)
cause I want to extract the word2vec embeddings of the words from the inputs like they show here
But I'm getting this error
TypeError Traceback (most recent call last)
/tmp/ipykernel_6732/2108362002.py in <module>
11 sgd = keras.optimizers.Adam(lr=0.0001)
12 model = Model(inputs=words_input, outputs=dense_2)
---> 13 extractor = Model(inputs=model.inputs, outputs=model.get_layer(words).output)
14 model.compile(loss='mean_squared_error', optimizer='adam', metrics=['acc'])
15 model.summary()
~/.local/lib/python3.8/site-packages/keras/engine/training.py in get_layer(self, name, index)
3271 if name is not None:
3272 for layer in self.layers:
-> 3273 if layer.name == name:
3274 return layer
3275 raise ValueError(
~/.local/lib/python3.8/site-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
~/.local/lib/python3.8/site-packages/keras/layers/core/tf_op_layer.py in handle(self, op, args, kwargs)
117 for x in tf.nest.flatten([args, kwargs])
118 ):
--> 119 return TFOpLambda(op)(*args, **kwargs)
120 else:
121 return self.NOT_SUPPORTED
~/.local/lib/python3.8/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
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
TypeError: Exception encountered when calling layer "tf.__operators__.eq" (type TFOpLambda).
Expected float32 passed to parameter 'y' of op 'Equal', got 'words_input' of type 'str' instead. Error: Expected float32, but got words_input of type 'str'.
Call arguments received by layer "tf.__operators__.eq" (type TFOpLambda):
• self=tf.Tensor(shape=(None, 500, 300), dtype=float32)
• other='words_input'
Any idea what I am doing wrong? Why is it passing the name of the first layer "words_input" to the parameter y? Which is what I assume it is doing?
You are not passing the correct name to the get_layer of the model, try this code
tf.keras.backend.clear_session()
words_input = keras.Input(shape=(500,),dtype='int32',name='words_input')
words = keras.layers.Embedding(input_dim=wordEmbeddings.shape[0], output_dim=wordEmbeddings.shape[1], weights=[wordEmbeddings], trainable=False, name='words')(words_input)
conv_1 = keras.layers.Conv1D(filters=100, kernel_size=10, strides=2, activation='relu')(words)
avgpool_1 = keras.layers.AveragePooling1D(pool_size=10, strides=10)(conv_1)
b_lstm = keras.layers.Bidirectional(keras.layers.LSTM(200, activation='tanh', return_sequences=False))(avgpool_1)
dense_1 = keras.layers.Dense(128, activation='relu')(b_lstm)
dropout = keras.layers.Dropout(0.1)(dense_1)
dense_2 = keras.layers.Dense(5, activation='softmax')(dropout)
sgd = keras.optimizers.Adam(learning_rate=0.0001)
model = keras.Model(inputs=words_input, outputs=dense_2)
extractor = keras.Model(inputs=model.inputs, outputs=model.get_layer('words').output)
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['acc'])
model.summary()
extractor.summary()
Output:
Related
I have this code:
EMBEDDING_DIM = 100
MAXLEN = 16
TRUNCATING = 'post'
PADDING = 'post'
OOV_TOKEN = "<OOV>"
MAX_EXAMPLES = 160000
TRAINING_SPLIT = 0.9
# Initialize an empty numpy array with the appropriate size
EMBEDDINGS_MATRIX = np.zeros((VOCAB_SIZE+1, EMBEDDING_DIM))
# Iterate all of the words in the vocabulary and if the vector representation for
# each word exists within GloVe's representations, save it in the EMBEDDINGS_MATRIX array
for word, i in word_index.items():
embedding_vector = GLOVE_EMBEDDINGS.get(word)
if embedding_vector is not None:
EMBEDDINGS_MATRIX[i] = embedding_vector
# Define the model
def create_model(vocab_size, embedding_dim, maxlen, embeddings_matrix):
model = tf.keras.Sequential([
# Set the Embedding layer when using pre-trained embeddings
tf.keras.layers.Embedding(vocab_size+1, embedding_dim, input_length=maxlen, weights=[embeddings_matrix], trainable=False),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Conv1D(64, 6, activation='relu'),
# tf.keras.layers.AveragePooling1D(pool_size=4),
tf.keras.layers.GlobalAveragePooling1D(),
tf.keras.layers.LSTM(64),
tf.keras.layers.Dense(8, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model.summary()
model = create_model(VOCAB_SIZE, EMBEDDING_DIM, MAXLEN, EMBEDDINGS_MATRIX)
# Train the model
history = model.fit(train_pad_trunc_seq, train_labels, epochs=20, validation_data=(val_pad_trunc_seq, val_labels))
which brings me this error,
ValueError Traceback (most recent call last)
Input In [26], in <cell line: 2>()
1 # Create your untrained model
----> 2 model = create_model(VOCAB_SIZE, EMBEDDING_DIM, MAXLEN, EMBEDDINGS_MATRIX)
4 # Train the model and save the training history
5 history = model.fit(train_pad_trunc_seq, train_labels, epochs=20, validation_data=(val_pad_trunc_seq, val_labels))
Input In [25], in create_model(vocab_size, embedding_dim, maxlen, embeddings_matrix)
4 def create_model(vocab_size, embedding_dim, maxlen, embeddings_matrix):
5
6 ### START CODE HERE
----> 8 model = tf.keras.Sequential([
9 # This is how you need to set the Embedding layer when using pre-trained embeddings
10 tf.keras.layers.Embedding(vocab_size+1, embedding_dim, input_length=maxlen, weights=[embeddings_matrix], trainable=False),
11 tf.keras.layers.Dropout(0.2),
12 tf.keras.layers.Conv1D(64, 6, activation='relu'),
13 # tf.keras.layers.AveragePooling1D(pool_size=4),
14 tf.keras.layers.GlobalAveragePooling1D(),
15 tf.keras.layers.LSTM(64),
16 tf.keras.layers.Dense(8, activation='relu'),
17 tf.keras.layers.Dense(1, activation='sigmoid')
18 ])
20 model.compile(loss='binary_crossentropy',
21 optimizer='adam',
22 metrics=['accuracy'])
24 ### END CODE HERE
File ~\.conda\envs\tf-gpu\lib\site-packages\tensorflow\python\training\tracking\base.py:629, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs)
627 self._self_setattr_tracking = False # pylint: disable=protected-access
628 try:
--> 629 result = method(self, *args, **kwargs)
630 finally:
631 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
File ~\.conda\envs\tf-gpu\lib\site-packages\keras\utils\traceback_utils.py:67, in filter_traceback.<locals>.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
File ~\.conda\envs\tf-gpu\lib\site-packages\keras\engine\input_spec.py:214, in assert_input_compatibility(input_spec, inputs, layer_name)
212 ndim = shape.rank
213 if ndim != spec.ndim:
--> 214 raise ValueError(f'Input {input_index} of layer "{layer_name}" '
215 'is incompatible with the layer: '
216 f'expected ndim={spec.ndim}, found ndim={ndim}. '
217 f'Full shape received: {tuple(shape)}')
218 if spec.max_ndim is not None:
219 ndim = x.shape.rank
ValueError: Input 0 of layer "lstm_2" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 64)
So I need to have an input parameter such as input = (?,?,?) for my LSTM layer instead of (None, 64), but what should it be?
I have also tried to change GlobalAveragePooling1D() to AveragePooling1D(pool_size=4).
It brings up the summary but gives me a different error:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_3 (Embedding) (None, 16, 100) 12829400
dropout_3 (Dropout) (None, 16, 100) 0
conv1d_3 (Conv1D) (None, 11, 64) 38464
average_pooling1d_1 (Averag (None, 2, 64) 0
ePooling1D)
lstm_3 (LSTM) (None, 64) 33024
dense_6 (Dense) (None, 8) 520
dense_7 (Dense) (None, 1) 9
=================================================================
Total params: 12,901,417
Trainable params: 72,017
Non-trainable params: 12,829,400
AttributeError Traceback (most recent call last)
Input In [24], in <cell line: 5>()
2 model = create_model(VOCAB_SIZE, EMBEDDING_DIM, MAXLEN, EMBEDDINGS_MATRIX)
4 # Train the model and save the training history
----> 5 history = model.fit(train_pad_trunc_seq, train_labels, epochs=20, validation_data=(val_pad_trunc_seq, val_labels))
AttributeError: 'NoneType' object has no attribute 'fit'
Please help?
I haven't used it before, but refer to https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D. It seems that if the input tensor is of dimension n, then GlobalAveragePooling 1D output a tensor with dimension n-1. GlobalAveragePooling1D do pooling along an axis, so it reduces the dim.
If the output tensor shape after conv1d is (None, 11, 64), then the output for GlobalAveragePooling 1D is (None, 64), which is of dimension 2, not 3, so the first attempt results in an error.
Things are different for AveragePooling1D. It does local average pooling, so the dimension of output tensor is the same as input tensor.
https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling1D
For the second question, refer to https://www.tensorflow.org/api_docs/python/tf/keras/Model#summary. model.summary() just prints a string summary of the network. (I guess the function return value is None). You should return a model with return model, because class Model has the method fit
I have built a BiLSTM model with an attention layer for sentence classification task but I am getting an error that my assertion has failed due to mismatch in number of parameters. The attention layer code is here and the error is below the code.
class attention(Layer):
def __init__(self, return_sequences=True):
self.return_sequences = return_sequences
super(attention,self).__init__()
def build(self, input_shape):
self.W=self.add_weight(name="att_weight", shape=(input_shape[-1],1),
initializer="normal")
self.b=self.add_weight(name="att_bias", shape=(input_shape[1],1),
initializer="zeros")
super(attention,self).build(input_shape)
def call(self, x):
e = K.tanh(K.dot(x,self.W)+self.b)
a = K.softmax(e, axis=1)
output = x*a
if self.return_sequences:
return output
return K.sum(output, axis=1)
When i am training the model with attention layer included, it is giving an error that assertion failed.
Epoch 1/10
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-45-ac310033130c> in <module>()
1 #Early stopping, Adam, dropout = 0.3, 0.5, 0.5
2 #history = model.fit(sequences_matrix, Y_train, batch_size=256, epochs=5, validation_split=0.1, callbacks=[EarlyStopping(monitor='val_loss', min_delta=0.0001)])
----> 3 history = model.fit(sequences_matrix, Y_train, batch_size=32, epochs=10, validation_split=0.1)
8 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: assertion failed: [Condition x == y did not hold element-wise:] [x (sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/Shape_1:0) = ] [32 1] [y (sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/strided_slice:0) = ] [32 758]
[[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert (defined at <ipython-input-45-ac310033130c>:3) ]] [Op:__inference_train_function_19854]
Function call stack:
train_function
My model is
model = Sequential()
model.add(Embedding(max_words, 768, input_length=max_len, weights=[embedding]))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(SpatialDropout1D(0.1))
model.add(Conv1D(16, kernel_size=11, activation='relu'))
model.add(Bidirectional(LSTM(16, return_sequences=True)))
model.add(attention(return_sequences=True))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Dropout(0.2))
model.add(Dense(2, activation='softmax', use_bias=True, kernel_regularizer=regularizers.l1_l2(l1=1e-5, l2=1e-4), bias_regularizer=regularizers.l2(1e-4),
activity_regularizer=regularizers.l2(1e-5)))
model.summary()
Shape of Y_train is
max_words = 48369
max_len = 768
tok = Tokenizer(num_words = max_words)
tok.fit_on_texts(X_train)
sequences = tok.texts_to_sequences(X_train)
sequences_matrix = sequence.pad_sequences(sequences, maxlen = max_len)
Y_train = np.array(Y_train)
Y_test = np.array(Y_test)
print(Y_train.shape)
(43532, 1)
your target is in 2D so you need to set return_sequences=False in the last attention layer in order to return output in 2D format
Add flatten layer before Dropout and then execute.
model.add(Flatten())
I am trying to fine-tune VGG16 neural network, here is the code:
vgg16_model = VGG16(weights="imagenet", include_top="false", input_shape=(224,224,3))
model = Sequential()
model.add(vgg16_model)
#add fully connected layer:
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))
I am getting this error:
ValueError Traceback (most recent call last)
in
2 model.add(vgg16_model)
3 #add fully connected layer:
----> 4 model.add(Flatten())
5 model.add(Dense(256, activation='relu'))
6 model.add(Dropout(0.5))
/usr/local/anaconda/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 '
/usr/local/anaconda/lib/python3.6/site-packages/keras/engine/base_layer.py in call(self, inputs, **kwargs)
412 # Raise exceptions in case the input is not compatible
413 # with the input_spec specified in the layer constructor.
--> 414 self.assert_input_compatibility(inputs)
415
416 # Collect input shapes to build layer.
/usr/local/anaconda/lib/python3.6/site-packages/keras/engine/base_layer.py in assert_input_compatibility(self, inputs)
325 self.name + ': expected min_ndim=' +
326 str(spec.min_ndim) + ', found ndim=' +
--> 327 str(K.ndim(x)))
328 # Check dtype.
329 if spec.dtype is not None:
ValueError: Input 0 is incompatible with layer flatten_5: expected min_ndim=3, found ndim=2
I tried many suggested solutions but none of them could solve my problem. How can I solve this?
In officially keras webpage, on
Fine-tune InceptionV3 on a new set of classes
from keras.models import Model
vgg16_model = VGG16(weights="imagenet", include_top="false", input_shape=(224,224,3))
x = vgg16_model.output
x=Flatten()(x)
x=Dense(256, activation='relu')(x)
x=Dropout(0.5)(x)
predictions=Dense(3, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
You have an error in include_top="false", this causes you the error message. Try:
vgg16_model = VGG16(weights="imagenet", include_top=False, input_shape=(224,224,3))
ConvLSTMCell Official Docs
GitHub _conv where the error occurs
Issue
I'm experimenting with the ConvLSTMCell in tensorflow r1.8. The error I'm continuing to generate occurs in the __call__ method of ConvLSTMCell. The _conv method is invoked and the error is raised.
ValueError: Conv Linear Expects 3D, 4D, 5D
The error is raised from the unstacked inputs. unstacked (in this example) has dimensions of [BATCH_SIZE, N_INPUTS] = [2,5]. I am using tf.unstack to generate the required sequence that the ConvLSTMCell requires.
Why use tf.unstack?
If the input array is not unstacked, the TypeError below is raised.
TypeError: inputs must be a sequence
Question
What am I missing on the formatting? I've read through related issues but have not found anything that has guided me into a working implementation.
Are the placeholder dimensions correct?
Should I be unstacking or is there a better way?
Am I providing the proper input dimension into the ConvLSTMCell?
Code
# Parameters
TIME_STEPS = 28
N_INPUT = 5
N_HIDDEN = 128
LEARNING_RATE = 0.001
NUM_UNITS = 28
CHANNEL = 1
tf.reset_default_graph()
# Input placeholders
x = tf.placeholder(tf.float32, [BATCH_SIZE, TIME_STEPS, N_INPUT])
y = tf.placeholder(tf.float32, [None, 1])
# Format input as a sequence for LSTM Input
unstacked = tf.unstack(x, TIME_STEPS, 1) # shape=(timesteps, batch, inputs)
# Convolutional LSTM Layer
lstm_layer = tf.contrib.rnn.ConvLSTMCell(
conv_ndims=1,
input_shape=[BATCH_SIZE, N_INPUT],
output_channels=5,
kernel_shape=[7,5]
)
# Error is generated when the lstm_layer is invoked
outputs, _ = tf.contrib.rnn.static_rnn(
lstm_layer,
unstacked,
dtype=tf.float32)
Error Message
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-83-3568a097e4ea> in <module>()
10 lstm_layer,
11 unstacked,
---> 12 dtype=tf.float32)
~/miniconda3/envs/MultivariateTimeSeries/lib/python3.6/site-packages/tensorflow/python/ops/rnn.py in static_rnn(cell, inputs, initial_state, dtype, sequence_length, scope)
1322 state_size=cell.state_size)
1323 else:
-> 1324 (output, state) = call_cell()
1325
1326 outputs.append(output)
~/miniconda3/envs/MultivariateTimeSeries/lib/python3.6/site-packages/tensorflow/python/ops/rnn.py in <lambda>()
1309 varscope.reuse_variables()
1310 # pylint: disable=cell-var-from-loop
-> 1311 call_cell = lambda: cell(input_, state)
1312 # pylint: enable=cell-var-from-loop
1313 if sequence_length is not None:
~/miniconda3/envs/MultivariateTimeSeries/lib/python3.6/site-packages/tensorflow/python/ops/rnn_cell_impl.py in __call__(self, inputs, state, scope)
230 setattr(self, scope_attrname, scope)
231 with scope:
--> 232 return super(RNNCell, self).__call__(inputs, state)
233
234 def _rnn_get_variable(self, getter, *args, **kwargs):
~/miniconda3/envs/MultivariateTimeSeries/lib/python3.6/site-packages/tensorflow/python/layers/base.py in __call__(self, inputs, *args, **kwargs)
715
716 if not in_deferred_mode:
--> 717 outputs = self.call(inputs, *args, **kwargs)
718 if outputs is None:
719 raise ValueError('A layer\'s `call` method should return a Tensor '
~/miniconda3/envs/MultivariateTimeSeries/lib/python3.6/site-packages/tensorflow/contrib/rnn/python/ops/rnn_cell.py in call(self, inputs, state, scope)
2110 cell, hidden = state
2111 new_hidden = _conv([inputs, hidden], self._kernel_shape,
-> 2112 4 * self._output_channels, self._use_bias)
2113 gates = array_ops.split(
2114 value=new_hidden, num_or_size_splits=4, axis=self._conv_ndims + 1)
~/miniconda3/envs/MultivariateTimeSeries/lib/python3.6/site-packages/tensorflow/contrib/rnn/python/ops/rnn_cell.py in _conv(args, filter_size, num_features, bias, bias_start)
2184 if len(shape) not in [3, 4, 5]:
2185 raise ValueError("Conv Linear expects 3D, 4D "
-> 2186 "or 5D arguments: %s" % str(shapes))
2187 if len(shape) != len(shapes[0]):
2188 raise ValueError("Conv Linear expects all args "
ValueError: Conv Linear expects 3D, 4D or 5D arguments: [[2, 5], [2, 2, 5]]
Here's an example with a couple tweaks, which at least passes static shape checking:
import tensorflow as tf
# Parameters
TIME_STEPS = 28
N_INPUT = 5
N_HIDDEN = 128
LEARNING_RATE = 0.001
NUM_UNITS = 28
CHANNEL = 1
BATCH_SIZE = 16
# Input placeholders
x = tf.placeholder(tf.float32, [BATCH_SIZE, TIME_STEPS, N_INPUT])
y = tf.placeholder(tf.float32, [None, 1])
# Format input as a sequence for LSTM Input
unstacked = tf.unstack(x[..., None], TIME_STEPS, 1) # shape=(timesteps, batch, inputs)
# Convolutional LSTM Layer
lstm_layer = tf.contrib.rnn.ConvLSTMCell(
conv_ndims=1,
input_shape=[N_INPUT, 1],
output_channels=5,
kernel_shape=[7]
)
# Error is generated when the lstm_layer is invoked
outputs, _ = tf.contrib.rnn.static_rnn(
lstm_layer,
unstacked,
dtype=tf.float32)
Notes:
input_shape does not include the batch dimension (see docstring)
The input needs a channels dimension. Fine for it to be one in the input (that's what I've done).
Not sure what more than one dimension on kernel_shape would mean for a 1-D convolution.
I am new to Keras and Theano, and now trying to implement my own loss function on Keras. But this error showed up. I thought the problem lies in my own loss function, but I have now idea how to fix it. Could someone help me figure this out?
import theano
import theano.tensor as T
def cost_estimation(y_true, y_pred):
for k in range(10):
d=T.log(1+T.exp((int(bool(y_true[k]==min(y_true)))*2-1)*(y_pred[k]-y_true[k])))
cost=cost+d
return d
The keras layers:
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='relu'))
#loss=keras.losses.categorical_crossentropy,
model.compile(loss='cost_estimation',
optimizer='adam',
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,
verbose=1, validation_data=(x_test, y_test))
Here is the error:
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
<ipython-input-6-d63101c47c94> in <module>()
130 model.compile(loss='cost_estimation',
131 optimizer='adam',
--> 132 metrics=['accuracy'])
133
134 model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,
/usr/local/lib/python2.7/dist-packages/keras/models.pyc in compile(self, optimizer, loss, metrics, sample_weight_mode, **kwargs)
764 metrics=metrics,
765 sample_weight_mode=sample_weight_mode,
--> 766 **kwargs)
767 self.optimizer = self.model.optimizer
768 self.loss = self.model.loss
/usr/local/lib/python2.7/dist-packages/keras/engine/training.pyc in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, **kwargs)
738 loss_functions = [losses.get(l) for l in loss]
739 else:
--> 740 loss_function = losses.get(loss)
741 loss_functions = [loss_function for _ in range(len(self.outputs))]
742 self.loss_functions = loss_functions
/usr/local/lib/python2.7/dist-packages/keras/losses.pyc in get(identifier)
88 if isinstance(identifier, six.string_types):
89 identifier = str(identifier)
---> 90 return deserialize(identifier)
91 elif callable(identifier):
92 return identifier
/usr/local/lib/python2.7/dist-packages/keras/losses.pyc in deserialize(name, custom_objects)
80 module_objects=globals(),
81 custom_objects=custom_objects,
---> 82 printable_module_name='loss function')
83
84
/usr/local/lib/python2.7/dist-packages/keras/utils/generic_utils.pyc in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
155 if fn is None:
156 raise ValueError('Unknown ' + printable_module_name,
--> 157 ':' + class_name)
158 return fn
159 else:
UnboundLocalError: local variable 'class_name' referenced before assignment
This seems to be an issue in keras codebase. It seems that if the a string is passed to the loss parameter this error arises. To fix this, pass
cost_estimation itself to loss, this way you avoid that branch of code.
model.compile(optimizer='rmsprop',
loss=cost_estimation, # not 'cost_estimation'
metrics=['accuracy'])