I built the model to classification using 1D convolution layers using tf.Keras .this works well if I remove the tensorboard.As I'm a beginner,I can't figure it out what the problem is. Please help
%reload_ext tensorboard
import tensorflow as tf
from tensorflow.keras.layers import Dense, Activation,Conv1D,MaxPool1D,GlobalAveragePooling1D,Dropout,Flatten,concatenate,Input
from tensorflow.keras.models import Model
inp = Input(shape=(1000,21))
a = Conv1D(filters=250,padding='valid', kernel_size=(8),strides=1)(inp)
a=MaxPool1D(1000-8+1,strides=1,padding='valid')(a)
a=Flatten()(a)
b = Conv1D(filters=250,padding='valid', kernel_size=(12))(inp)
b=MaxPool1D(1000-12+1,strides=1,padding='valid')(b)
b=Flatten()(b)
c = Conv1D(filters=250,padding='valid', kernel_size=(16))(inp)
c=MaxPool1D(1000-16+1,strides=1,padding='valid')(c)
c=Flatten()(c)
d = Conv1D(filters=250,padding='valid', kernel_size=(20))(inp)
d=MaxPool1D(1000-20+1,strides=1,padding='valid')(d)
d=Flatten()(d)
e = Conv1D(filters=250,padding='valid', kernel_size=(24))(inp)
e=MaxPool1D(1000-24+1,strides=1,padding='valid')(e)
e=Flatten()(e)
f = Conv1D(filters=250,padding='valid', kernel_size=(28))(inp)
f=MaxPool1D(1000-28+1,strides=1,padding='valid')(f)
f=Flatten()(f)
g = Conv1D(filters=250,padding='valid', kernel_size=(32))(inp)
g=MaxPool1D(1000-32+1,strides=1,padding='valid')(g)
g=Flatten()(g)
h= Conv1D(filters=250,padding='valid', kernel_size=(36))(inp)
h=MaxPool1D(1000-36+1,strides=1,padding='valid')(h)
h=Flatten()(h)
model=concatenate([a,b,c,d,e,f,g,h],1)
model = Dropout(0.3)(model)
#model = Dense(2000,activation='relu')(model)
model = Dense(2892,activation='sigmoid')(model)
model = Model(inp, model)
print(model.summary())
and use the sequence class batch generator to input batches
class MyGenerator(tf.keras.utils.Sequence):
'Generates data for Keras'
def __init__(self, ids, train_dir):
'Initialization'
self.ids = ids
self.train_dir = train_dir
def __len__(self):
'Denotes the number of batches per epoch'
batch_size=100
numofBatchs=math.ceil(len(self.train_dir) / batch_size)
return numofBatchs
def __getitem__(self, index):
test_resL=[]
test_resD=[]
start_posT = index * 100
end_posT = min(start_posT + 100, len(self.train_dir))
test_resL=self.ids[start_posT:end_posT]
test_resD=self.train_dir[start_posT:end_posT]
#iTest += 1
isize=len(test_resL)
dataTest = np.zeros( (isize,1000,21), dtype=np.float32 )
labelsTest = np.zeros( (isize,2892), dtype=np.uint8 )
#rawTest = []
for i , idy in enumerate(test_resL):
label=idy
#print(label)
encoding_label_np(label, labelsTest[i] )
for i , idx in enumerate(test_resD):
seq=idx
encoding_seq_np(seq, dataTest[i] )
#dataShped=np.reshape(dataTest,(-1, 1, 1000, 21))
# print(dataTest[0:2])
# print(labelsTest[0:2])
return dataTest,labelsTest
after adding tensor board it runs 1st epoch only.
poch 1/20
10431/10431 [==============================] - ETA: 0s - loss: 2.7652 - accuracy: 0.3496
---------------------------------------------------------------------------
_FallbackException Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_summary_ops.py in write_histogram_summary(writer, step, tag, values, name)
463 _ctx._context_handle, tld.device_name, "WriteHistogramSummary", name,
--> 464 tld.op_callbacks, writer, step, tag, values)
465 return _result
_FallbackException: This function does not handle the case of the path where all inputs are not already EagerTensors.
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
13 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: Nan in summary histogram for: conv1d_16/kernel_0 [Op:WriteHistogramSummary]
Providing the solution here (Answer Section), even though it is present in the comment section for the benefit of the community.
InvalidArgumentError: Nan in summary histogram for: conv1d_16/kernel_0 [Op:WriteHistogramSummary]
This issue was resolved after modifying activation function from sigmoid to softmax in the dense layer of Model.
Final Model Structure is as below
import tensorflow as tf
from tensorflow.keras.layers import Dense, Activation,Conv1D,MaxPool1D,GlobalAveragePooling1D,Dropout,Flatten,concatenate,Input
from tensorflow.keras.models import Model
inp = Input(shape=(1000,21))
a = Conv1D(filters=250,padding='valid', kernel_size=(8),strides=1)(inp)
a=MaxPool1D(1000-8+1,strides=1,padding='valid')(a)
a=Flatten()(a)
b = Conv1D(filters=250,padding='valid', kernel_size=(12))(inp)
b=MaxPool1D(1000-12+1,strides=1,padding='valid')(b)
b=Flatten()(b)
c = Conv1D(filters=250,padding='valid', kernel_size=(16))(inp)
c=MaxPool1D(1000-16+1,strides=1,padding='valid')(c)
c=Flatten()(c)
d = Conv1D(filters=250,padding='valid', kernel_size=(20))(inp)
d=MaxPool1D(1000-20+1,strides=1,padding='valid')(d)
d=Flatten()(d)
e = Conv1D(filters=250,padding='valid', kernel_size=(24))(inp)
e=MaxPool1D(1000-24+1,strides=1,padding='valid')(e)
e=Flatten()(e)
f = Conv1D(filters=250,padding='valid', kernel_size=(28))(inp)
f=MaxPool1D(1000-28+1,strides=1,padding='valid')(f)
f=Flatten()(f)
g = Conv1D(filters=250,padding='valid', kernel_size=(32))(inp)
g=MaxPool1D(1000-32+1,strides=1,padding='valid')(g)
g=Flatten()(g)
h= Conv1D(filters=250,padding='valid', kernel_size=(36))(inp)
h=MaxPool1D(1000-36+1,strides=1,padding='valid')(h)
h=Flatten()(h)
model=concatenate([a,b,c,d,e,f,g,h],1)
model = Dropout(0.3)(model)
#model = Dense(2000,activation='relu')(model)
model = Dense(2892,activation='softmax')(model)
model = Model(inp, model)
print(model.summary())
Output:
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 1000, 21)] 0
__________________________________________________________________________________________________
conv1d (Conv1D) (None, 993, 250) 42250 input_1[0][0]
__________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, 989, 250) 63250 input_1[0][0]
__________________________________________________________________________________________________
conv1d_2 (Conv1D) (None, 985, 250) 84250 input_1[0][0]
__________________________________________________________________________________________________
conv1d_3 (Conv1D) (None, 981, 250) 105250 input_1[0][0]
__________________________________________________________________________________________________
conv1d_4 (Conv1D) (None, 977, 250) 126250 input_1[0][0]
__________________________________________________________________________________________________
conv1d_5 (Conv1D) (None, 973, 250) 147250 input_1[0][0]
__________________________________________________________________________________________________
conv1d_6 (Conv1D) (None, 969, 250) 168250 input_1[0][0]
__________________________________________________________________________________________________
conv1d_7 (Conv1D) (None, 965, 250) 189250 input_1[0][0]
__________________________________________________________________________________________________
max_pooling1d (MaxPooling1D) (None, 1, 250) 0 conv1d[0][0]
__________________________________________________________________________________________________
max_pooling1d_1 (MaxPooling1D) (None, 1, 250) 0 conv1d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_2 (MaxPooling1D) (None, 1, 250) 0 conv1d_2[0][0]
__________________________________________________________________________________________________
max_pooling1d_3 (MaxPooling1D) (None, 1, 250) 0 conv1d_3[0][0]
__________________________________________________________________________________________________
max_pooling1d_4 (MaxPooling1D) (None, 1, 250) 0 conv1d_4[0][0]
__________________________________________________________________________________________________
max_pooling1d_5 (MaxPooling1D) (None, 1, 250) 0 conv1d_5[0][0]
__________________________________________________________________________________________________
max_pooling1d_6 (MaxPooling1D) (None, 1, 250) 0 conv1d_6[0][0]
__________________________________________________________________________________________________
max_pooling1d_7 (MaxPooling1D) (None, 1, 250) 0 conv1d_7[0][0]
__________________________________________________________________________________________________
flatten (Flatten) (None, 250) 0 max_pooling1d[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 250) 0 max_pooling1d_1[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 250) 0 max_pooling1d_2[0][0]
__________________________________________________________________________________________________
flatten_3 (Flatten) (None, 250) 0 max_pooling1d_3[0][0]
__________________________________________________________________________________________________
flatten_4 (Flatten) (None, 250) 0 max_pooling1d_4[0][0]
__________________________________________________________________________________________________
flatten_5 (Flatten) (None, 250) 0 max_pooling1d_5[0][0]
__________________________________________________________________________________________________
flatten_6 (Flatten) (None, 250) 0 max_pooling1d_6[0][0]
__________________________________________________________________________________________________
flatten_7 (Flatten) (None, 250) 0 max_pooling1d_7[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 2000) 0 flatten[0][0]
flatten_1[0][0]
flatten_2[0][0]
flatten_3[0][0]
flatten_4[0][0]
flatten_5[0][0]
flatten_6[0][0]
flatten_7[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 2000) 0 concatenate[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 2892) 5786892 dropout[0][0]
==================================================================================================
Total params: 6,712,892
Trainable params: 6,712,892
Non-trainable params: 0
______________________________________