Environment:
- Tensorflow: 2.3.0 (CPU only)
- Python: 3.8.5
- GPU: 0
- OS: Ubuntu 20.04 LTS
Problem Statement:
I'd like to apologise for asking another newbie question, but i'm trying to load model using load_model() method in Tensorflow (CPU only version).
I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 1996330000 Hz
I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fc360269ab0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
Attempt:
I tried setting environment variable link
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import tensorflow as tf
from keras.models import load_model
model = tf.keras.models.load_model('path/to/location/model.model')
Or
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import tensorflow as tf
from keras.models import load_model
model = tf.keras.models.load_model('path/to/location/model.model')
Note: Please check that model is in
.modelextension
Q1. Is there anyway to inspect my model which is in .model extension?
Edit:
As per @kosa answer model.summary() giving me following output.
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
image (InputLayer) [(None, 45, 168, 1)] 0
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 45, 168, 16) 160 image[0][0]
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 23, 84, 16) 0 conv2d[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 23, 84, 32) 4640 max_pooling2d[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 12, 42, 32) 0 conv2d_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 12, 42, 32) 9248 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 6, 21, 32) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_v1 (BatchNo (None, 6, 21, 32) 128 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
flatten (Flatten) (None, 4032) 0 batch_normalization_v1[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 64) 258112 flatten[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 64) 258112 flatten[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 64) 258112 flatten[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 64) 258112 flatten[0][0]
__________________________________________________________________________________________________
dense_4 (Dense) (None, 64) 258112 flatten[0][0]
__________________________________________________________________________________________________
dense_5 (Dense) (None, 64) 258112 flatten[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 64) 0 dense[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 64) 0 dense_1[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 64) 0 dense_2[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 64) 0 dense_3[0][0]
__________________________________________________________________________________________________
dropout_4 (Dropout) (None, 64) 0 dense_4[0][0]
__________________________________________________________________________________________________
dropout_5 (Dropout) (None, 64) 0 dense_5[0][0]
__________________________________________________________________________________________________
char_1 (Dense) (None, 36) 2340 dropout[0][0]
__________________________________________________________________________________________________
char_2 (Dense) (None, 36) 2340 dropout_1[0][0]
__________________________________________________________________________________________________
char_3 (Dense) (None, 36) 2340 dropout_2[0][0]
__________________________________________________________________________________________________
char_4 (Dense) (None, 36) 2340 dropout_3[0][0]
__________________________________________________________________________________________________
char_5 (Dense) (None, 36) 2340 dropout_4[0][0]
__________________________________________________________________________________________________
char_6 (Dense) (None, 36) 2340 dropout_5[0][0]
==================================================================================================
Total params: 1,576,888
Trainable params: 1,576,824
Non-trainable params: 64
__________________________________________________________________________________________________
None
