- when it comes to following a proper diet plan, the trainees might get lazy
- replace the prescribed meal with what they think might be equivalent to it in calories
- skip meal/too many cheat days
- algorithm Development using Python
- exploring CNN
- Converting model into tensorflow lite(tflite) such that I can be integrated with Flutter App
- development and efficient working of mobile app using Flutter
An Flutter application which when feed with an image it will classify it and outputs its label.
import numpy as np
import pandas as pd
from pathlib import Path
import os.path
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing import image
import tensorflow as tf
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
from sklearn.metrics import confusion_matrix, classification_report
import timeInit Plugin
Init Graph Optimizer
Init Kernel
image_dir = Path(r'/Users/rafay/Documents/FYP/abc')filepaths = list(image_dir.glob(r'**/*.jpg'))
labels = list(map(lambda x: os.path.split(os.path.split(x)[0])[1], filepaths))
filepaths = pd.Series(filepaths, name='Filepath').astype(str)
labels = pd.Series(labels, name='Label')
images = pd.concat([filepaths, labels], axis=1)
category_samples = []
for category in images['Label'].unique():
category_slice = images.query("Label == @category")
category_samples.append(category_slice.sample(100, random_state=1))
image_df = pd.concat(category_samples, axis=0).sample(frac=1.0, random_state=1).reset_index(drop=True)abc = list(labels)num_c = int(len(abc)/1000)num_c3
image_df['Label'].value_counts()waffles 100
onion_rings 100
samosa 100
Name: Label, dtype: int64
image_df['Label']0 waffles
1 waffles
2 waffles
3 onion_rings
4 waffles
...
295 onion_rings
296 onion_rings
297 samosa
298 onion_rings
299 samosa
Name: Label, Length: 300, dtype: object
train_df, test_df = train_test_split(image_df, train_size=0.7, shuffle=True, random_state=1)train_generator = tf.keras.preprocessing.image.ImageDataGenerator(
preprocessing_function=tf.keras.applications.mobilenet_v2.preprocess_input,
validation_split=0.2
)
test_generator = tf.keras.preprocessing.image.ImageDataGenerator(
preprocessing_function=tf.keras.applications.mobilenet_v2.preprocess_input
)train_images = train_generator.flow_from_dataframe(
dataframe=train_df,
x_col='Filepath',
y_col='Label',
target_size=(224, 224),
color_mode='rgb',
class_mode='categorical',
batch_size=32,
shuffle=True,
seed=42,
subset='training'
)
val_images = train_generator.flow_from_dataframe(
dataframe=train_df,
x_col='Filepath',
y_col='Label',
target_size=(224, 224),
color_mode='rgb',
class_mode='categorical',
batch_size=32,
shuffle=True,
seed=42,
subset='validation'
)
test_images = test_generator.flow_from_dataframe(
dataframe=test_df,
x_col='Filepath',
y_col='Label',
target_size=(224, 224),
color_mode='rgb',
class_mode='categorical',
batch_size=32,
shuffle=False
)Found 168 validated image filenames belonging to 3 classes.
Found 42 validated image filenames belonging to 3 classes.
Found 90 validated image filenames belonging to 3 classes.
batch_size = 32
img_height = 228
img_width = 228
channels = 3
img_shape = (img_height, img_width, channels)pre_trained = InceptionV3(weights='imagenet', include_top=False, input_shape=img_shape, pooling='avg')
for layer in pre_trained.layers:
layer.trainable = FalseMetal device set to: Apple M1
2022-08-13 17:54:48.919208: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2022-08-13 17:54:48.919302: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
x = pre_trained.output
x = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(x)
x = Dropout(0.2)(x)
x = Dense(1024, activation='LeakyReLU')(x)
x = Dropout(0.2)(x)
predictions = Dense(num_c, activation='softmax')(x)
model = Model(inputs = pre_trained.input, outputs = predictions)
model.compile(optimizer = Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])model.summary()Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 228, 228, 3) 0
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 113, 113, 32) 864 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 113, 113, 32) 96 conv2d[0][0]
__________________________________________________________________________________________________
activation (Activation) (None, 113, 113, 32) 0 batch_normalization[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 111, 111, 32) 9216 activation[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 111, 111, 32) 96 conv2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 111, 111, 32) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 111, 111, 64) 18432 activation_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 111, 111, 64) 192 conv2d_2[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 111, 111, 64) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 55, 55, 64) 0 activation_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 55, 55, 80) 5120 max_pooling2d[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 55, 55, 80) 240 conv2d_3[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 55, 55, 80) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 53, 53, 192) 138240 activation_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 53, 53, 192) 576 conv2d_4[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 53, 53, 192) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 26, 26, 192) 0 activation_4[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 26, 26, 64) 12288 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 26, 26, 64) 192 conv2d_8[0][0]
__________________________________________________________________________________________________
activation_8 (Activation) (None, 26, 26, 64) 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 26, 26, 48) 9216 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 26, 26, 96) 55296 activation_8[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 26, 26, 48) 144 conv2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 26, 26, 96) 288 conv2d_9[0][0]
__________________________________________________________________________________________________
activation_6 (Activation) (None, 26, 26, 48) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
activation_9 (Activation) (None, 26, 26, 96) 0 batch_normalization_9[0][0]
__________________________________________________________________________________________________
average_pooling2d (AveragePooli (None, 26, 26, 192) 0 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 26, 26, 64) 12288 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 26, 26, 64) 76800 activation_6[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 26, 26, 96) 82944 activation_9[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 26, 26, 32) 6144 average_pooling2d[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 26, 26, 64) 192 conv2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 26, 26, 64) 192 conv2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 26, 26, 96) 288 conv2d_10[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 26, 26, 32) 96 conv2d_11[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 26, 26, 64) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
activation_7 (Activation) (None, 26, 26, 64) 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
activation_10 (Activation) (None, 26, 26, 96) 0 batch_normalization_10[0][0]
__________________________________________________________________________________________________
activation_11 (Activation) (None, 26, 26, 32) 0 batch_normalization_11[0][0]
__________________________________________________________________________________________________
mixed0 (Concatenate) (None, 26, 26, 256) 0 activation_5[0][0]
activation_7[0][0]
activation_10[0][0]
activation_11[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 26, 26, 64) 16384 mixed0[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 26, 26, 64) 192 conv2d_15[0][0]
__________________________________________________________________________________________________
activation_15 (Activation) (None, 26, 26, 64) 0 batch_normalization_15[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 26, 26, 48) 12288 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 26, 26, 96) 55296 activation_15[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 26, 26, 48) 144 conv2d_13[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 26, 26, 96) 288 conv2d_16[0][0]
__________________________________________________________________________________________________
activation_13 (Activation) (None, 26, 26, 48) 0 batch_normalization_13[0][0]
__________________________________________________________________________________________________
activation_16 (Activation) (None, 26, 26, 96) 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, 26, 26, 256) 0 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 26, 26, 64) 16384 mixed0[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 26, 26, 64) 76800 activation_13[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 26, 26, 96) 82944 activation_16[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 26, 26, 64) 16384 average_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 26, 26, 64) 192 conv2d_12[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 26, 26, 64) 192 conv2d_14[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 26, 26, 96) 288 conv2d_17[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 26, 26, 64) 192 conv2d_18[0][0]
__________________________________________________________________________________________________
activation_12 (Activation) (None, 26, 26, 64) 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
activation_14 (Activation) (None, 26, 26, 64) 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
activation_17 (Activation) (None, 26, 26, 96) 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
activation_18 (Activation) (None, 26, 26, 64) 0 batch_normalization_18[0][0]
__________________________________________________________________________________________________
mixed1 (Concatenate) (None, 26, 26, 288) 0 activation_12[0][0]
activation_14[0][0]
activation_17[0][0]
activation_18[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 26, 26, 64) 18432 mixed1[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 26, 26, 64) 192 conv2d_22[0][0]
__________________________________________________________________________________________________
activation_22 (Activation) (None, 26, 26, 64) 0 batch_normalization_22[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 26, 26, 48) 13824 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 26, 26, 96) 55296 activation_22[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 26, 26, 48) 144 conv2d_20[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 26, 26, 96) 288 conv2d_23[0][0]
__________________________________________________________________________________________________
activation_20 (Activation) (None, 26, 26, 48) 0 batch_normalization_20[0][0]
__________________________________________________________________________________________________
activation_23 (Activation) (None, 26, 26, 96) 0 batch_normalization_23[0][0]
__________________________________________________________________________________________________
average_pooling2d_2 (AveragePoo (None, 26, 26, 288) 0 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 26, 26, 64) 18432 mixed1[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 26, 26, 64) 76800 activation_20[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 26, 26, 96) 82944 activation_23[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, 26, 26, 64) 18432 average_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 26, 26, 64) 192 conv2d_19[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 26, 26, 64) 192 conv2d_21[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 26, 26, 96) 288 conv2d_24[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 26, 26, 64) 192 conv2d_25[0][0]
__________________________________________________________________________________________________
activation_19 (Activation) (None, 26, 26, 64) 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
activation_21 (Activation) (None, 26, 26, 64) 0 batch_normalization_21[0][0]
__________________________________________________________________________________________________
activation_24 (Activation) (None, 26, 26, 96) 0 batch_normalization_24[0][0]
__________________________________________________________________________________________________
activation_25 (Activation) (None, 26, 26, 64) 0 batch_normalization_25[0][0]
__________________________________________________________________________________________________
mixed2 (Concatenate) (None, 26, 26, 288) 0 activation_19[0][0]
activation_21[0][0]
activation_24[0][0]
activation_25[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D) (None, 26, 26, 64) 18432 mixed2[0][0]
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 26, 26, 64) 192 conv2d_27[0][0]
__________________________________________________________________________________________________
activation_27 (Activation) (None, 26, 26, 64) 0 batch_normalization_27[0][0]
__________________________________________________________________________________________________
conv2d_28 (Conv2D) (None, 26, 26, 96) 55296 activation_27[0][0]
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 26, 26, 96) 288 conv2d_28[0][0]
__________________________________________________________________________________________________
activation_28 (Activation) (None, 26, 26, 96) 0 batch_normalization_28[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D) (None, 12, 12, 384) 995328 mixed2[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D) (None, 12, 12, 96) 82944 activation_28[0][0]
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 12, 12, 384) 1152 conv2d_26[0][0]
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 12, 12, 96) 288 conv2d_29[0][0]
__________________________________________________________________________________________________
activation_26 (Activation) (None, 12, 12, 384) 0 batch_normalization_26[0][0]
__________________________________________________________________________________________________
activation_29 (Activation) (None, 12, 12, 96) 0 batch_normalization_29[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 12, 12, 288) 0 mixed2[0][0]
__________________________________________________________________________________________________
mixed3 (Concatenate) (None, 12, 12, 768) 0 activation_26[0][0]
activation_29[0][0]
max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_34 (Conv2D) (None, 12, 12, 128) 98304 mixed3[0][0]
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 12, 12, 128) 384 conv2d_34[0][0]
__________________________________________________________________________________________________
activation_34 (Activation) (None, 12, 12, 128) 0 batch_normalization_34[0][0]
__________________________________________________________________________________________________
conv2d_35 (Conv2D) (None, 12, 12, 128) 114688 activation_34[0][0]
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 12, 12, 128) 384 conv2d_35[0][0]
__________________________________________________________________________________________________
activation_35 (Activation) (None, 12, 12, 128) 0 batch_normalization_35[0][0]
__________________________________________________________________________________________________
conv2d_31 (Conv2D) (None, 12, 12, 128) 98304 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_36 (Conv2D) (None, 12, 12, 128) 114688 activation_35[0][0]
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 12, 12, 128) 384 conv2d_31[0][0]
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 12, 12, 128) 384 conv2d_36[0][0]
__________________________________________________________________________________________________
activation_31 (Activation) (None, 12, 12, 128) 0 batch_normalization_31[0][0]
__________________________________________________________________________________________________
activation_36 (Activation) (None, 12, 12, 128) 0 batch_normalization_36[0][0]
__________________________________________________________________________________________________
conv2d_32 (Conv2D) (None, 12, 12, 128) 114688 activation_31[0][0]
__________________________________________________________________________________________________
conv2d_37 (Conv2D) (None, 12, 12, 128) 114688 activation_36[0][0]
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 12, 12, 128) 384 conv2d_32[0][0]
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 12, 12, 128) 384 conv2d_37[0][0]
__________________________________________________________________________________________________
activation_32 (Activation) (None, 12, 12, 128) 0 batch_normalization_32[0][0]
__________________________________________________________________________________________________
activation_37 (Activation) (None, 12, 12, 128) 0 batch_normalization_37[0][0]
__________________________________________________________________________________________________
average_pooling2d_3 (AveragePoo (None, 12, 12, 768) 0 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_30 (Conv2D) (None, 12, 12, 192) 147456 mixed3[0][0]
__________________________________________________________________________________________________
conv2d_33 (Conv2D) (None, 12, 12, 192) 172032 activation_32[0][0]
__________________________________________________________________________________________________
conv2d_38 (Conv2D) (None, 12, 12, 192) 172032 activation_37[0][0]
__________________________________________________________________________________________________
conv2d_39 (Conv2D) (None, 12, 12, 192) 147456 average_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 12, 12, 192) 576 conv2d_30[0][0]
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 12, 12, 192) 576 conv2d_33[0][0]
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 12, 12, 192) 576 conv2d_38[0][0]
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 12, 12, 192) 576 conv2d_39[0][0]
__________________________________________________________________________________________________
activation_30 (Activation) (None, 12, 12, 192) 0 batch_normalization_30[0][0]
__________________________________________________________________________________________________
activation_33 (Activation) (None, 12, 12, 192) 0 batch_normalization_33[0][0]
__________________________________________________________________________________________________
activation_38 (Activation) (None, 12, 12, 192) 0 batch_normalization_38[0][0]
__________________________________________________________________________________________________
activation_39 (Activation) (None, 12, 12, 192) 0 batch_normalization_39[0][0]
__________________________________________________________________________________________________
mixed4 (Concatenate) (None, 12, 12, 768) 0 activation_30[0][0]
activation_33[0][0]
activation_38[0][0]
activation_39[0][0]
__________________________________________________________________________________________________
conv2d_44 (Conv2D) (None, 12, 12, 160) 122880 mixed4[0][0]
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, 12, 12, 160) 480 conv2d_44[0][0]
__________________________________________________________________________________________________
activation_44 (Activation) (None, 12, 12, 160) 0 batch_normalization_44[0][0]
__________________________________________________________________________________________________
conv2d_45 (Conv2D) (None, 12, 12, 160) 179200 activation_44[0][0]
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, 12, 12, 160) 480 conv2d_45[0][0]
__________________________________________________________________________________________________
activation_45 (Activation) (None, 12, 12, 160) 0 batch_normalization_45[0][0]
__________________________________________________________________________________________________
conv2d_41 (Conv2D) (None, 12, 12, 160) 122880 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_46 (Conv2D) (None, 12, 12, 160) 179200 activation_45[0][0]
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 12, 12, 160) 480 conv2d_41[0][0]
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, 12, 12, 160) 480 conv2d_46[0][0]
__________________________________________________________________________________________________
activation_41 (Activation) (None, 12, 12, 160) 0 batch_normalization_41[0][0]
__________________________________________________________________________________________________
activation_46 (Activation) (None, 12, 12, 160) 0 batch_normalization_46[0][0]
__________________________________________________________________________________________________
conv2d_42 (Conv2D) (None, 12, 12, 160) 179200 activation_41[0][0]
__________________________________________________________________________________________________
conv2d_47 (Conv2D) (None, 12, 12, 160) 179200 activation_46[0][0]
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 12, 12, 160) 480 conv2d_42[0][0]
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, 12, 12, 160) 480 conv2d_47[0][0]
__________________________________________________________________________________________________
activation_42 (Activation) (None, 12, 12, 160) 0 batch_normalization_42[0][0]
__________________________________________________________________________________________________
activation_47 (Activation) (None, 12, 12, 160) 0 batch_normalization_47[0][0]
__________________________________________________________________________________________________
average_pooling2d_4 (AveragePoo (None, 12, 12, 768) 0 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_40 (Conv2D) (None, 12, 12, 192) 147456 mixed4[0][0]
__________________________________________________________________________________________________
conv2d_43 (Conv2D) (None, 12, 12, 192) 215040 activation_42[0][0]
__________________________________________________________________________________________________
conv2d_48 (Conv2D) (None, 12, 12, 192) 215040 activation_47[0][0]
__________________________________________________________________________________________________
conv2d_49 (Conv2D) (None, 12, 12, 192) 147456 average_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 12, 12, 192) 576 conv2d_40[0][0]
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, 12, 12, 192) 576 conv2d_43[0][0]
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, 12, 12, 192) 576 conv2d_48[0][0]
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, 12, 12, 192) 576 conv2d_49[0][0]
__________________________________________________________________________________________________
activation_40 (Activation) (None, 12, 12, 192) 0 batch_normalization_40[0][0]
__________________________________________________________________________________________________
activation_43 (Activation) (None, 12, 12, 192) 0 batch_normalization_43[0][0]
__________________________________________________________________________________________________
activation_48 (Activation) (None, 12, 12, 192) 0 batch_normalization_48[0][0]
__________________________________________________________________________________________________
activation_49 (Activation) (None, 12, 12, 192) 0 batch_normalization_49[0][0]
__________________________________________________________________________________________________
mixed5 (Concatenate) (None, 12, 12, 768) 0 activation_40[0][0]
activation_43[0][0]
activation_48[0][0]
activation_49[0][0]
__________________________________________________________________________________________________
conv2d_54 (Conv2D) (None, 12, 12, 160) 122880 mixed5[0][0]
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, 12, 12, 160) 480 conv2d_54[0][0]
__________________________________________________________________________________________________
activation_54 (Activation) (None, 12, 12, 160) 0 batch_normalization_54[0][0]
__________________________________________________________________________________________________
conv2d_55 (Conv2D) (None, 12, 12, 160) 179200 activation_54[0][0]
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, 12, 12, 160) 480 conv2d_55[0][0]
__________________________________________________________________________________________________
activation_55 (Activation) (None, 12, 12, 160) 0 batch_normalization_55[0][0]
__________________________________________________________________________________________________
conv2d_51 (Conv2D) (None, 12, 12, 160) 122880 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_56 (Conv2D) (None, 12, 12, 160) 179200 activation_55[0][0]
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, 12, 12, 160) 480 conv2d_51[0][0]
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, 12, 12, 160) 480 conv2d_56[0][0]
__________________________________________________________________________________________________
activation_51 (Activation) (None, 12, 12, 160) 0 batch_normalization_51[0][0]
__________________________________________________________________________________________________
activation_56 (Activation) (None, 12, 12, 160) 0 batch_normalization_56[0][0]
__________________________________________________________________________________________________
conv2d_52 (Conv2D) (None, 12, 12, 160) 179200 activation_51[0][0]
__________________________________________________________________________________________________
conv2d_57 (Conv2D) (None, 12, 12, 160) 179200 activation_56[0][0]
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, 12, 12, 160) 480 conv2d_52[0][0]
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, 12, 12, 160) 480 conv2d_57[0][0]
__________________________________________________________________________________________________
activation_52 (Activation) (None, 12, 12, 160) 0 batch_normalization_52[0][0]
__________________________________________________________________________________________________
activation_57 (Activation) (None, 12, 12, 160) 0 batch_normalization_57[0][0]
__________________________________________________________________________________________________
average_pooling2d_5 (AveragePoo (None, 12, 12, 768) 0 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_50 (Conv2D) (None, 12, 12, 192) 147456 mixed5[0][0]
__________________________________________________________________________________________________
conv2d_53 (Conv2D) (None, 12, 12, 192) 215040 activation_52[0][0]
__________________________________________________________________________________________________
conv2d_58 (Conv2D) (None, 12, 12, 192) 215040 activation_57[0][0]
__________________________________________________________________________________________________
conv2d_59 (Conv2D) (None, 12, 12, 192) 147456 average_pooling2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, 12, 12, 192) 576 conv2d_50[0][0]
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, 12, 12, 192) 576 conv2d_53[0][0]
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, 12, 12, 192) 576 conv2d_58[0][0]
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, 12, 12, 192) 576 conv2d_59[0][0]
__________________________________________________________________________________________________
activation_50 (Activation) (None, 12, 12, 192) 0 batch_normalization_50[0][0]
__________________________________________________________________________________________________
activation_53 (Activation) (None, 12, 12, 192) 0 batch_normalization_53[0][0]
__________________________________________________________________________________________________
activation_58 (Activation) (None, 12, 12, 192) 0 batch_normalization_58[0][0]
__________________________________________________________________________________________________
activation_59 (Activation) (None, 12, 12, 192) 0 batch_normalization_59[0][0]
__________________________________________________________________________________________________
mixed6 (Concatenate) (None, 12, 12, 768) 0 activation_50[0][0]
activation_53[0][0]
activation_58[0][0]
activation_59[0][0]
__________________________________________________________________________________________________
conv2d_64 (Conv2D) (None, 12, 12, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, 12, 12, 192) 576 conv2d_64[0][0]
__________________________________________________________________________________________________
activation_64 (Activation) (None, 12, 12, 192) 0 batch_normalization_64[0][0]
__________________________________________________________________________________________________
conv2d_65 (Conv2D) (None, 12, 12, 192) 258048 activation_64[0][0]
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, 12, 12, 192) 576 conv2d_65[0][0]
__________________________________________________________________________________________________
activation_65 (Activation) (None, 12, 12, 192) 0 batch_normalization_65[0][0]
__________________________________________________________________________________________________
conv2d_61 (Conv2D) (None, 12, 12, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_66 (Conv2D) (None, 12, 12, 192) 258048 activation_65[0][0]
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, 12, 12, 192) 576 conv2d_61[0][0]
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, 12, 12, 192) 576 conv2d_66[0][0]
__________________________________________________________________________________________________
activation_61 (Activation) (None, 12, 12, 192) 0 batch_normalization_61[0][0]
__________________________________________________________________________________________________
activation_66 (Activation) (None, 12, 12, 192) 0 batch_normalization_66[0][0]
__________________________________________________________________________________________________
conv2d_62 (Conv2D) (None, 12, 12, 192) 258048 activation_61[0][0]
__________________________________________________________________________________________________
conv2d_67 (Conv2D) (None, 12, 12, 192) 258048 activation_66[0][0]
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, 12, 12, 192) 576 conv2d_62[0][0]
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, 12, 12, 192) 576 conv2d_67[0][0]
__________________________________________________________________________________________________
activation_62 (Activation) (None, 12, 12, 192) 0 batch_normalization_62[0][0]
__________________________________________________________________________________________________
activation_67 (Activation) (None, 12, 12, 192) 0 batch_normalization_67[0][0]
__________________________________________________________________________________________________
average_pooling2d_6 (AveragePoo (None, 12, 12, 768) 0 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_60 (Conv2D) (None, 12, 12, 192) 147456 mixed6[0][0]
__________________________________________________________________________________________________
conv2d_63 (Conv2D) (None, 12, 12, 192) 258048 activation_62[0][0]
__________________________________________________________________________________________________
conv2d_68 (Conv2D) (None, 12, 12, 192) 258048 activation_67[0][0]
__________________________________________________________________________________________________
conv2d_69 (Conv2D) (None, 12, 12, 192) 147456 average_pooling2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, 12, 12, 192) 576 conv2d_60[0][0]
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, 12, 12, 192) 576 conv2d_63[0][0]
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, 12, 12, 192) 576 conv2d_68[0][0]
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, 12, 12, 192) 576 conv2d_69[0][0]
__________________________________________________________________________________________________
activation_60 (Activation) (None, 12, 12, 192) 0 batch_normalization_60[0][0]
__________________________________________________________________________________________________
activation_63 (Activation) (None, 12, 12, 192) 0 batch_normalization_63[0][0]
__________________________________________________________________________________________________
activation_68 (Activation) (None, 12, 12, 192) 0 batch_normalization_68[0][0]
__________________________________________________________________________________________________
activation_69 (Activation) (None, 12, 12, 192) 0 batch_normalization_69[0][0]
__________________________________________________________________________________________________
mixed7 (Concatenate) (None, 12, 12, 768) 0 activation_60[0][0]
activation_63[0][0]
activation_68[0][0]
activation_69[0][0]
__________________________________________________________________________________________________
conv2d_72 (Conv2D) (None, 12, 12, 192) 147456 mixed7[0][0]
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, 12, 12, 192) 576 conv2d_72[0][0]
__________________________________________________________________________________________________
activation_72 (Activation) (None, 12, 12, 192) 0 batch_normalization_72[0][0]
__________________________________________________________________________________________________
conv2d_73 (Conv2D) (None, 12, 12, 192) 258048 activation_72[0][0]
__________________________________________________________________________________________________
batch_normalization_73 (BatchNo (None, 12, 12, 192) 576 conv2d_73[0][0]
__________________________________________________________________________________________________
activation_73 (Activation) (None, 12, 12, 192) 0 batch_normalization_73[0][0]
__________________________________________________________________________________________________
conv2d_70 (Conv2D) (None, 12, 12, 192) 147456 mixed7[0][0]
__________________________________________________________________________________________________
conv2d_74 (Conv2D) (None, 12, 12, 192) 258048 activation_73[0][0]
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, 12, 12, 192) 576 conv2d_70[0][0]
__________________________________________________________________________________________________
batch_normalization_74 (BatchNo (None, 12, 12, 192) 576 conv2d_74[0][0]
__________________________________________________________________________________________________
activation_70 (Activation) (None, 12, 12, 192) 0 batch_normalization_70[0][0]
__________________________________________________________________________________________________
activation_74 (Activation) (None, 12, 12, 192) 0 batch_normalization_74[0][0]
__________________________________________________________________________________________________
conv2d_71 (Conv2D) (None, 5, 5, 320) 552960 activation_70[0][0]
__________________________________________________________________________________________________
conv2d_75 (Conv2D) (None, 5, 5, 192) 331776 activation_74[0][0]
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, 5, 5, 320) 960 conv2d_71[0][0]
__________________________________________________________________________________________________
batch_normalization_75 (BatchNo (None, 5, 5, 192) 576 conv2d_75[0][0]
__________________________________________________________________________________________________
activation_71 (Activation) (None, 5, 5, 320) 0 batch_normalization_71[0][0]
__________________________________________________________________________________________________
activation_75 (Activation) (None, 5, 5, 192) 0 batch_normalization_75[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 5, 5, 768) 0 mixed7[0][0]
__________________________________________________________________________________________________
mixed8 (Concatenate) (None, 5, 5, 1280) 0 activation_71[0][0]
activation_75[0][0]
max_pooling2d_3[0][0]
__________________________________________________________________________________________________
conv2d_80 (Conv2D) (None, 5, 5, 448) 573440 mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_80 (BatchNo (None, 5, 5, 448) 1344 conv2d_80[0][0]
__________________________________________________________________________________________________
activation_80 (Activation) (None, 5, 5, 448) 0 batch_normalization_80[0][0]
__________________________________________________________________________________________________
conv2d_77 (Conv2D) (None, 5, 5, 384) 491520 mixed8[0][0]
__________________________________________________________________________________________________
conv2d_81 (Conv2D) (None, 5, 5, 384) 1548288 activation_80[0][0]
__________________________________________________________________________________________________
batch_normalization_77 (BatchNo (None, 5, 5, 384) 1152 conv2d_77[0][0]
__________________________________________________________________________________________________
batch_normalization_81 (BatchNo (None, 5, 5, 384) 1152 conv2d_81[0][0]
__________________________________________________________________________________________________
activation_77 (Activation) (None, 5, 5, 384) 0 batch_normalization_77[0][0]
__________________________________________________________________________________________________
activation_81 (Activation) (None, 5, 5, 384) 0 batch_normalization_81[0][0]
__________________________________________________________________________________________________
conv2d_78 (Conv2D) (None, 5, 5, 384) 442368 activation_77[0][0]
__________________________________________________________________________________________________
conv2d_79 (Conv2D) (None, 5, 5, 384) 442368 activation_77[0][0]
__________________________________________________________________________________________________
conv2d_82 (Conv2D) (None, 5, 5, 384) 442368 activation_81[0][0]
__________________________________________________________________________________________________
conv2d_83 (Conv2D) (None, 5, 5, 384) 442368 activation_81[0][0]
__________________________________________________________________________________________________
average_pooling2d_7 (AveragePoo (None, 5, 5, 1280) 0 mixed8[0][0]
__________________________________________________________________________________________________
conv2d_76 (Conv2D) (None, 5, 5, 320) 409600 mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_78 (BatchNo (None, 5, 5, 384) 1152 conv2d_78[0][0]
__________________________________________________________________________________________________
batch_normalization_79 (BatchNo (None, 5, 5, 384) 1152 conv2d_79[0][0]
__________________________________________________________________________________________________
batch_normalization_82 (BatchNo (None, 5, 5, 384) 1152 conv2d_82[0][0]
__________________________________________________________________________________________________
batch_normalization_83 (BatchNo (None, 5, 5, 384) 1152 conv2d_83[0][0]
__________________________________________________________________________________________________
conv2d_84 (Conv2D) (None, 5, 5, 192) 245760 average_pooling2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_76 (BatchNo (None, 5, 5, 320) 960 conv2d_76[0][0]
__________________________________________________________________________________________________
activation_78 (Activation) (None, 5, 5, 384) 0 batch_normalization_78[0][0]
__________________________________________________________________________________________________
activation_79 (Activation) (None, 5, 5, 384) 0 batch_normalization_79[0][0]
__________________________________________________________________________________________________
activation_82 (Activation) (None, 5, 5, 384) 0 batch_normalization_82[0][0]
__________________________________________________________________________________________________
activation_83 (Activation) (None, 5, 5, 384) 0 batch_normalization_83[0][0]
__________________________________________________________________________________________________
batch_normalization_84 (BatchNo (None, 5, 5, 192) 576 conv2d_84[0][0]
__________________________________________________________________________________________________
activation_76 (Activation) (None, 5, 5, 320) 0 batch_normalization_76[0][0]
__________________________________________________________________________________________________
mixed9_0 (Concatenate) (None, 5, 5, 768) 0 activation_78[0][0]
activation_79[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 5, 5, 768) 0 activation_82[0][0]
activation_83[0][0]
__________________________________________________________________________________________________
activation_84 (Activation) (None, 5, 5, 192) 0 batch_normalization_84[0][0]
__________________________________________________________________________________________________
mixed9 (Concatenate) (None, 5, 5, 2048) 0 activation_76[0][0]
mixed9_0[0][0]
concatenate[0][0]
activation_84[0][0]
__________________________________________________________________________________________________
conv2d_89 (Conv2D) (None, 5, 5, 448) 917504 mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_89 (BatchNo (None, 5, 5, 448) 1344 conv2d_89[0][0]
__________________________________________________________________________________________________
activation_89 (Activation) (None, 5, 5, 448) 0 batch_normalization_89[0][0]
__________________________________________________________________________________________________
conv2d_86 (Conv2D) (None, 5, 5, 384) 786432 mixed9[0][0]
__________________________________________________________________________________________________
conv2d_90 (Conv2D) (None, 5, 5, 384) 1548288 activation_89[0][0]
__________________________________________________________________________________________________
batch_normalization_86 (BatchNo (None, 5, 5, 384) 1152 conv2d_86[0][0]
__________________________________________________________________________________________________
batch_normalization_90 (BatchNo (None, 5, 5, 384) 1152 conv2d_90[0][0]
__________________________________________________________________________________________________
activation_86 (Activation) (None, 5, 5, 384) 0 batch_normalization_86[0][0]
__________________________________________________________________________________________________
activation_90 (Activation) (None, 5, 5, 384) 0 batch_normalization_90[0][0]
__________________________________________________________________________________________________
conv2d_87 (Conv2D) (None, 5, 5, 384) 442368 activation_86[0][0]
__________________________________________________________________________________________________
conv2d_88 (Conv2D) (None, 5, 5, 384) 442368 activation_86[0][0]
__________________________________________________________________________________________________
conv2d_91 (Conv2D) (None, 5, 5, 384) 442368 activation_90[0][0]
__________________________________________________________________________________________________
conv2d_92 (Conv2D) (None, 5, 5, 384) 442368 activation_90[0][0]
__________________________________________________________________________________________________
average_pooling2d_8 (AveragePoo (None, 5, 5, 2048) 0 mixed9[0][0]
__________________________________________________________________________________________________
conv2d_85 (Conv2D) (None, 5, 5, 320) 655360 mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_87 (BatchNo (None, 5, 5, 384) 1152 conv2d_87[0][0]
__________________________________________________________________________________________________
batch_normalization_88 (BatchNo (None, 5, 5, 384) 1152 conv2d_88[0][0]
__________________________________________________________________________________________________
batch_normalization_91 (BatchNo (None, 5, 5, 384) 1152 conv2d_91[0][0]
__________________________________________________________________________________________________
batch_normalization_92 (BatchNo (None, 5, 5, 384) 1152 conv2d_92[0][0]
__________________________________________________________________________________________________
conv2d_93 (Conv2D) (None, 5, 5, 192) 393216 average_pooling2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_85 (BatchNo (None, 5, 5, 320) 960 conv2d_85[0][0]
__________________________________________________________________________________________________
activation_87 (Activation) (None, 5, 5, 384) 0 batch_normalization_87[0][0]
__________________________________________________________________________________________________
activation_88 (Activation) (None, 5, 5, 384) 0 batch_normalization_88[0][0]
__________________________________________________________________________________________________
activation_91 (Activation) (None, 5, 5, 384) 0 batch_normalization_91[0][0]
__________________________________________________________________________________________________
activation_92 (Activation) (None, 5, 5, 384) 0 batch_normalization_92[0][0]
__________________________________________________________________________________________________
batch_normalization_93 (BatchNo (None, 5, 5, 192) 576 conv2d_93[0][0]
__________________________________________________________________________________________________
activation_85 (Activation) (None, 5, 5, 320) 0 batch_normalization_85[0][0]
__________________________________________________________________________________________________
mixed9_1 (Concatenate) (None, 5, 5, 768) 0 activation_87[0][0]
activation_88[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 5, 5, 768) 0 activation_91[0][0]
activation_92[0][0]
__________________________________________________________________________________________________
activation_93 (Activation) (None, 5, 5, 192) 0 batch_normalization_93[0][0]
__________________________________________________________________________________________________
mixed10 (Concatenate) (None, 5, 5, 2048) 0 activation_85[0][0]
mixed9_1[0][0]
concatenate_1[0][0]
activation_93[0][0]
__________________________________________________________________________________________________
global_average_pooling2d (Globa (None, 2048) 0 mixed10[0][0]
__________________________________________________________________________________________________
batch_normalization_94 (BatchNo (None, 2048) 8192 global_average_pooling2d[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 2048) 0 batch_normalization_94[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 1024) 2098176 dropout[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 1024) 0 dense[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 3) 3075 dropout_1[0][0]
==================================================================================================
Total params: 23,912,227
Trainable params: 2,105,347
Non-trainable params: 21,806,880
__________________________________________________________________________________________________
STEP_SIZE_TRAIN = train_images.n // train_images.batch_size
STEP_SIZE_VALID = val_images.n // val_images.batch_size
history = model.fit_generator(train_images,
steps_per_epoch = STEP_SIZE_TRAIN,
validation_data = val_images,
validation_steps = STEP_SIZE_VALID,
epochs = 50,
verbose = 1)Epoch 1/50
/opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/tensorflow/python/keras/engine/training.py:1940: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
warnings.warn('`Model.fit_generator` is deprecated and '
2022-08-13 17:54:50.060562: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
2022-08-13 17:54:50.060678: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
2022-08-13 17:54:50.962506: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
5/5 [==============================] - ETA: 0s - loss: 1.4046 - accuracy: 0.4926
2022-08-13 17:54:52.844974: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
5/5 [==============================] - 3s 444ms/step - loss: 1.4046 - accuracy: 0.4926 - val_loss: 0.7372 - val_accuracy: 0.6875
Epoch 2/50
5/5 [==============================] - 1s 240ms/step - loss: 0.5208 - accuracy: 0.8015 - val_loss: 0.4757 - val_accuracy: 0.7812
Epoch 3/50
5/5 [==============================] - 1s 272ms/step - loss: 0.3311 - accuracy: 0.9000 - val_loss: 0.4184 - val_accuracy: 0.7812
Epoch 4/50
5/5 [==============================] - 1s 242ms/step - loss: 0.2162 - accuracy: 0.9338 - val_loss: 0.3193 - val_accuracy: 0.8438
Epoch 5/50
5/5 [==============================] - 1s 244ms/step - loss: 0.1625 - accuracy: 0.9338 - val_loss: 0.2076 - val_accuracy: 0.9062
Epoch 6/50
5/5 [==============================] - 1s 239ms/step - loss: 0.0953 - accuracy: 0.9706 - val_loss: 0.1723 - val_accuracy: 0.9688
Epoch 7/50
5/5 [==============================] - 1s 236ms/step - loss: 0.0750 - accuracy: 0.9706 - val_loss: 0.1891 - val_accuracy: 0.9062
Epoch 8/50
5/5 [==============================] - 1s 239ms/step - loss: 0.0737 - accuracy: 0.9706 - val_loss: 0.1509 - val_accuracy: 0.9375
Epoch 9/50
5/5 [==============================] - 1s 240ms/step - loss: 0.0607 - accuracy: 0.9853 - val_loss: 0.1962 - val_accuracy: 0.9062
Epoch 10/50
5/5 [==============================] - 1s 271ms/step - loss: 0.0400 - accuracy: 0.9875 - val_loss: 0.1693 - val_accuracy: 0.9375
Epoch 11/50
5/5 [==============================] - 1s 268ms/step - loss: 0.0216 - accuracy: 1.0000 - val_loss: 0.1879 - val_accuracy: 0.9062
Epoch 12/50
5/5 [==============================] - 1s 235ms/step - loss: 0.0281 - accuracy: 1.0000 - val_loss: 0.1530 - val_accuracy: 0.9375
Epoch 13/50
5/5 [==============================] - 1s 248ms/step - loss: 0.0153 - accuracy: 1.0000 - val_loss: 0.2082 - val_accuracy: 0.8750
Epoch 14/50
5/5 [==============================] - 1s 239ms/step - loss: 0.0175 - accuracy: 1.0000 - val_loss: 0.1235 - val_accuracy: 0.9375
Epoch 15/50
5/5 [==============================] - 1s 278ms/step - loss: 0.0179 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.8750
Epoch 16/50
5/5 [==============================] - 1s 239ms/step - loss: 0.0108 - accuracy: 1.0000 - val_loss: 0.1641 - val_accuracy: 0.9062
Epoch 17/50
5/5 [==============================] - 1s 244ms/step - loss: 0.0206 - accuracy: 0.9926 - val_loss: 0.1286 - val_accuracy: 0.9375
Epoch 18/50
5/5 [==============================] - 1s 254ms/step - loss: 0.0494 - accuracy: 0.9779 - val_loss: 0.1055 - val_accuracy: 0.9375
Epoch 19/50
5/5 [==============================] - 1s 249ms/step - loss: 0.0133 - accuracy: 0.9926 - val_loss: 0.1542 - val_accuracy: 0.9062
Epoch 20/50
5/5 [==============================] - 1s 243ms/step - loss: 0.0141 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.8750
Epoch 21/50
5/5 [==============================] - 1s 243ms/step - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.8750
Epoch 22/50
5/5 [==============================] - 1s 244ms/step - loss: 0.0096 - accuracy: 1.0000 - val_loss: 0.1616 - val_accuracy: 0.9062
Epoch 23/50
5/5 [==============================] - 1s 249ms/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.1660 - val_accuracy: 0.9062
Epoch 24/50
5/5 [==============================] - 1s 237ms/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.8750
Epoch 25/50
5/5 [==============================] - 1s 278ms/step - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.1794 - val_accuracy: 0.9062
Epoch 26/50
5/5 [==============================] - 1s 284ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.1189 - val_accuracy: 0.9375
Epoch 27/50
5/5 [==============================] - 1s 241ms/step - loss: 0.0146 - accuracy: 0.9926 - val_loss: 0.1466 - val_accuracy: 0.9062
Epoch 28/50
5/5 [==============================] - 1s 241ms/step - loss: 0.0105 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9375
Epoch 29/50
5/5 [==============================] - 1s 240ms/step - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.1579 - val_accuracy: 0.9062
Epoch 30/50
5/5 [==============================] - 1s 273ms/step - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.1318 - val_accuracy: 0.9375
Epoch 31/50
5/5 [==============================] - 1s 239ms/step - loss: 0.0082 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9375
Epoch 32/50
5/5 [==============================] - 1s 236ms/step - loss: 0.0091 - accuracy: 0.9926 - val_loss: 0.1629 - val_accuracy: 0.9062
Epoch 33/50
5/5 [==============================] - 1s 241ms/step - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.1223 - val_accuracy: 0.9375
Epoch 34/50
5/5 [==============================] - 1s 268ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.1362 - val_accuracy: 0.9688
Epoch 35/50
5/5 [==============================] - 1s 252ms/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9688
Epoch 36/50
5/5 [==============================] - 1s 272ms/step - loss: 0.0151 - accuracy: 1.0000 - val_loss: 0.1564 - val_accuracy: 0.9688
Epoch 37/50
5/5 [==============================] - 1s 240ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.1479 - val_accuracy: 0.9688
Epoch 38/50
5/5 [==============================] - 1s 247ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.1611 - val_accuracy: 0.9688
Epoch 39/50
5/5 [==============================] - 1s 247ms/step - loss: 0.0082 - accuracy: 1.0000 - val_loss: 0.1516 - val_accuracy: 0.9688
Epoch 40/50
5/5 [==============================] - 1s 277ms/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 0.1425 - val_accuracy: 0.9688
Epoch 41/50
5/5 [==============================] - 1s 245ms/step - loss: 0.0097 - accuracy: 1.0000 - val_loss: 0.0564 - val_accuracy: 1.0000
Epoch 42/50
5/5 [==============================] - 1s 277ms/step - loss: 0.0084 - accuracy: 1.0000 - val_loss: 0.1642 - val_accuracy: 0.9688
Epoch 43/50
5/5 [==============================] - 1s 243ms/step - loss: 0.0405 - accuracy: 0.9853 - val_loss: 0.1092 - val_accuracy: 0.9375
Epoch 44/50
5/5 [==============================] - 1s 241ms/step - loss: 0.0306 - accuracy: 0.9853 - val_loss: 0.1571 - val_accuracy: 0.9375
Epoch 45/50
5/5 [==============================] - 1s 275ms/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9062
Epoch 46/50
5/5 [==============================] - 1s 245ms/step - loss: 0.0155 - accuracy: 1.0000 - val_loss: 0.2573 - val_accuracy: 0.9062
Epoch 47/50
5/5 [==============================] - 1s 245ms/step - loss: 0.0394 - accuracy: 0.9853 - val_loss: 0.2192 - val_accuracy: 0.9062
Epoch 48/50
5/5 [==============================] - 1s 241ms/step - loss: 0.0132 - accuracy: 0.9926 - val_loss: 0.1512 - val_accuracy: 0.9375
Epoch 49/50
5/5 [==============================] - 1s 275ms/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.1686 - val_accuracy: 0.9062
Epoch 50/50
5/5 [==============================] - 1s 259ms/step - loss: 0.0202 - accuracy: 0.9926 - val_loss: 0.1130 - val_accuracy: 0.9375
results = model.evaluate(test_images, verbose=0)
print("Test Accuracy: {:.2f}%".format(results[1] * 100))Test Accuracy: 94.44%
plt.xlabel('Epoch Number')
plt.ylabel('Loss')
plt.plot(history.history['loss'], label='training set')
plt.plot(history.history['val_loss'], label='test set')
plt.legend()<matplotlib.legend.Legend at 0x17f12aee0>
plt.xlabel('Epoch Number')
plt.ylabel('Accuracy')
plt.plot(history.history['accuracy'], label='training set')
plt.plot(history.history['val_accuracy'], label='test set')
plt.legend()<matplotlib.legend.Legend at 0x17f18d8b0>
predictions = np.argmax(model.predict(test_images), axis=1)
cm = confusion_matrix(test_images.labels, predictions)
clr = classification_report(test_images.labels, predictions,
target_names=test_images.class_indices,
zero_division=0)2022-08-13 17:55:57.101428: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
plt.figure(figsize=(30, 30))
sns.heatmap(cm, annot=True, fmt='g', vmin=0, cmap='Blues', cbar=False)
plt.xticks(ticks=np.arange(num_c) + 0.5, labels=test_images.class_indices, rotation=90)
plt.yticks(ticks=np.arange(num_c) + 0.5, labels=test_images.class_indices, rotation=0)
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.title("Confusion Matrix")
plt.show()