I am trying to create a callback for extracting the per-epoch outputs in the 1st hidden layer of my model. With what I have written, self.model.layers[0].output outputs a Tensor object but I could not see the actual entries.
Ideally I would like to save these output tensors, and visualise using an epoch vs mean-output plot. This has been implemented in Glorot & Bengio (2010) but the source code is not available.
How shall I edit my code in order to make the model fitting process save the outputs in each epoch? Thanks in advance.
class PerEpochOutputCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs=None):
print('First layer output of epoch:', epoch+1, self.model.layers[0].output)
model_relu_3= Sequential()
# Use ReLU hidden layer
model_relu_3.add(Dense(3, input_dim= 8, activation= 'relu', kernel_initializer= 'uniform'))
model_relu_3.add(Dense(5, input_dim= 3, activation= 'relu', kernel_initializer= 'uniform'))
model_relu_3.add(Dense(5, input_dim= 5, activation= 'relu', kernel_initializer= 'uniform'))
model_relu_3.add(Dense(1, activation= 'sigmoid', kernel_initializer= 'uniform'))
model_relu_3.compile(loss='binary_crossentropy', optimizer='adam', metrics= ['accuracy'])
# Train model
tr_results = model_relu_3.fit(X, y, validation_split=0.2, epochs=10, batch_size=32,
verbose=2, callbacks=[PerEpochOutputCallback()])
====
Train on 614 samples, validate on 154 samples
Epoch 1/10
First layer output of epoch: 1 Tensor("dense_42/Relu:0", shape=(None, 3), dtype=float32)
614/614 - 0s - loss: 0.6915 - accuracy: 0.6531 - val_loss: 0.6897 - val_accuracy: 0.6429
Epoch 2/10
First layer output of epoch: 2 Tensor("dense_42/Relu:0", shape=(None, 3), dtype=float32)
614/614 - 0s - loss: 0.6874 - accuracy: 0.6531 - val_loss: 0.6853 - val_accuracy: 0.6429
Epoch 3/10
First layer output of epoch: 3 Tensor("dense_42/Relu:0", shape=(None, 3), dtype=float32)
614/614 - 0s - loss: 0.6824 - accuracy: 0.6531 - val_loss: 0.6783 - val_accuracy: 0.6429
Related
I have used a Tensorboard callback in fitting a model consisting of one embedding layer and one SimpleRNN layer. The model performs binary sentiment classification for 9600 input text sequences. They have been tokenised and padded in advance.
# 1. Remove previous logs
!rm -rf ./logs/
# 2. Change to Py_file_dir
os.chdir(...)
# input_dim = 43489 (size of tokenizer word dictionary); output_dim = 100 (GloVe 100d embeddings); input_length = 1403 (length of longest text sequence).
# xtr_pad is padded, tokenised text sequences. nrow = 9600, ncol = input_length = 1403.
model= Sequential()
model.add(Embedding(input_dim, output_dim, input_length= input_length,
weights= [Embedding_matrix], trainable= False))
model.add(SimpleRNN(200))
model.add(Dense(1, activation= 'sigmoid'))
model.compile(loss='binary_crossentropy', optimizer= 'adam', metrics=['accuracy'])
tb = TensorBoard(histogram_freq=1, log_dir= 'tbcallback_prac')
tr_results= model.fit(xtr_pad, ytr, epochs= 2, batch_size= 64, verbose= 1,
validation_split= 0.2, callbacks= [tb])
# In command prompt enter: tensorboard --logdir tbcallback_prac
I have run this on Jupyterlab and on the first time the model trains without issue. I was able to view the Tensorboard statistics on local host.
However when I run this same code a second time, i.e. removing logs and fitting model it completed the first epoch of training, but returns this error before the 2nd epoch begins.
Train on 7680 samples, validate on 1920 samples
Epoch 1/2
7680/7680 [==============================] - ETA: 0s - loss: 0.2919 - accuracy: 0.9004
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-12-a1cde9b5b1f4> in <module>()
7 tb = TensorBoard(histogram_freq=1, log_dir= 'tbcallback_prac')
8 tr_results= model.fit(xtr_pad, ytr, epochs= 2, batch_size= 64, verbose= 1,
----> 9 validation_split= 0.2, callbacks= [tb])
...
InvalidArgumentError: You must feed a value for placeholder tensor 'embedding_input' with dtype float and shape [?,1403]
[[{{node embedding_input}}]]
Note 1403 is the length of all padded, tokenised sequences in training input 'xtr'.
Thanks in advance for any help!
I have no issue but I think that is a dimensions problem when working on logtis and sigmoid
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 3072, 64) 64000
simple_rnn (SimpleRNN) (None, 200) 53000
dense (Dense) (None, 1) 201
=================================================================
Total params: 117,201
Trainable params: 117,201
Non-trainable params: 0
_________________________________________________________________
val_dir: F:\models\checkpoint\ale_highscores_3\validation
Epoch 1/1500
2/2 [==============================] - ETA: 0s - loss: -0.5579 - accuracy: 0.1000[<KerasTensor: shape=(None, 3072) dtype=float32 (created by layer 'embedding_input')>]
<keras.engine.functional.Functional object at 0x00000233003A8550>
Press AnyKey!
2/2 [==============================] - 14s 7s/step - loss: -0.5579 - accuracy: 0.1000 - val_loss: -0.6446 - val_accuracy: 0.1000
Epoch 2/1500
2/2 [==============================] - ETA: 0s - loss: -0.6588 - accuracy: 0.1000[<KerasTensor: shape=(None, 3072) dtype=float32 (created by layer 'embedding_input')>]
<keras.engine.functional.Functional object at 0x00000233003A8C40>
Press AnyKey!
2/2 [==============================] - 13s 7s/step - loss: -0.6588 - accuracy: 0.1000 - val_loss: -0.7242 - val_accuracy: 0.1000
Epoch 3/1500
1/2 [==============>...............] - ETA: 6s - loss: -0.1867 - accuracy: 0.1429
I am working on predicting stock trend (up, or down).
Below is how I am handling my pre-processing.
index_ = len(df.columns) - 1
x = df.iloc[:,:index_]
x = x[['Relative_Volume', 'CurrentPrice', 'MarketCap']]
x = x.values.astype(float)
# x = x.reshape(len(x), 1, x.shape[1]).astype(float)
x = x.reshape(*x.shape, 1)
y = df.iloc[:,index_:].values.astype(float)
# x.shape = (44930, 3, 1)
# y.shape = (44930, 1)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=98 )
Then I am building my BILSTM model:
def build_nn():
model = Sequential()
model.add(Bidirectional(LSTM(128, return_sequences=True, input_shape = (x_train.shape[0], 1) , name="one")))
model.add(Dropout(0.20))
model.add(Bidirectional(LSTM(128, return_sequences=True , name="two")))
model.add(Dropout(0.20))
model.add(Bidirectional(LSTM(64, return_sequences=False , name="three")))
model.add(Dropout(0.20))
model.add(Dense(1,activation='sigmoid'))
# opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)
opt = SGD(lr=0.01)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
return model
filepath = "bilstmv1.h5"
chkp = ModelCheckpoint(monitor = 'val_accuracy', mode = 'auto', filepath=filepath, verbose = 1, save_best_only=True)
model = build_nn()
# model.summary()
model.fit(x_train, y_train,
epochs=3,
batch_size=256,
validation_split=0.1, callbacks=[chkp])
model.summary()
Below is the output of the loss_value:
Epoch 1/3
127/127 [==============================] - 27s 130ms/step - loss: 0.6829 - accuracy: 0.5845 - val_loss: 0.6797 - val_accuracy: 0.5803
Epoch 00001: val_accuracy improved from -inf to 0.58025, saving model to bilstmv1.h5
Epoch 2/3
127/127 [==============================] - 14s 112ms/step - loss: 0.6788 - accuracy: 0.5851 - val_loss: 0.6798 - val_accuracy: 0.5803
Epoch 00002: val_accuracy did not improve from 0.58025
Epoch 3/3
127/127 [==============================] - 14s 112ms/step - loss: 0.6800 - accuracy: 0.5822 - val_loss: 0.6798 - val_accuracy: 0.5803
Epoch 00003: val_accuracy did not improve from 0.58025
I have tried to change the optimzer, loss_function, and other modification. As you can expect, all the predictions are same since the loss function is not being changed.
You have an issue with your input shape in your first LSTM layer. Keras inputs takes (None, Your_Shape) as its input, since your input to the model can vary. You can have 1 input, 2 inputs, or infinity inputs. The only way to represent dynamic is by using None as the first input. The quickest way to do this is to change the input to (None, *input_shape), since the * will expand your input shape.
Your build function will then become:
def build_nn():
model = Sequential()
model.add(Bidirectional(LSTM(128, return_sequences=True, input_shape = (None, *x_train.shape) , name="one")))
model.add(Dropout(0.20))
model.add(Bidirectional(LSTM(128, return_sequences=True , name="two")))
model.add(Dropout(0.20))
model.add(Bidirectional(LSTM(64, return_sequences=False , name="three")))
model.add(Dropout(0.20))
model.add(Dense(1,activation='sigmoid'))
# opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)
opt = SGD(lr=0.01)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])
return model
Though I still advise having a look at your Optimizer as that might affect your results. You can also use -1 as an input shape which will mean auto fill, but you can only use it once.
I am fitting a large CNN network on my training data, validating on 20%. It appears the model performs better on the training than the validation set. What do you suggest so I can improve the model performance.
CNN Architecture:
model = Sequential()
activ = 'relu'
model.add(Conv2D(32, (1, 3), strides=(1, 1), padding='same', activation=activ, input_shape=(1, 100, 4)))
model.add(Conv2D(32, (1, 3), strides=(1, 1), padding='same', activation=activ))
#model.add(BatchNormalization(axis = 3))
model.add(MaxPooling2D(pool_size=(1, 2) ))
model.add(Conv2D(64, (1, 3), strides=(1, 1), padding='same', activation=activ))
model.add(Conv2D(64, (1, 3), strides=(1, 1), padding='same', activation=activ))
model.add(MaxPooling2D(pool_size=(1, 2)))
model.add(Conv2D(128, (1, 3), strides=(1, 1), padding='same', activation=activ))
model.add(Conv2D(128, (1, 3), strides=(1, 1), padding='same', activation=activ ))
model.add(MaxPooling2D(pool_size=(1, 2)))
model.add(Dropout(.5))
model.add(Flatten())
A = model.output_shape
model.add(Dense(int(A[1] * 1/4.), activation=activ))
model.add(Dropout(.5))
model.add(Dense(5, activation='softmax'))
optimizer = Adam(lr=0.003, beta_1=0.9, beta_2=0.999, epsilon=1e-04, decay=0.0)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=100, batch_size=64, shuffle=False,
validation_split=0.2)
However, the validation accuracy doesn't change for the number of epochs.
Epoch 1/100
1065/1065 [==============================] - 14s 13ms/step - loss: 1.4174 - accuracy: 0.5945 - val_loss: 1.4966 - val_accuracy: 0.4417
Epoch 2/100
1065/1065 [==============================] - 14s 13ms/step - loss: 1.1494 - accuracy: 0.6207 - val_loss: 1.4634 - val_accuracy: 0.4417
Epoch 3/100
1065/1065 [==============================] - 19s 18ms/step - loss: 1.1111 - accuracy: 0.6196 - val_loss: 1.4674 - val_accuracy: 0.4417
Epoch 4/100
1065/1065 [==============================] - 15s 14ms/step - loss: 1.1040 - accuracy: 0.6196 - val_loss: 1.4660 - val_accuracy: 0.4417
Epoch 5/100
1065/1065 [==============================] - 18s 17ms/step - loss: 1.1027 - accuracy: 0.6196 - val_loss: 1.4624 - val_accuracy: 0.4417
NOTE: I Adam´s default learning rate 0.001 as well as 0.003 but the output is the same (log).
Your model is working but improving very slowly. I would reduce the dropout value down to .1 initially, then run the model and see if it overfits or not.If it does then slowly increase the dropout rate. Unless your data is already shuffled I would set shuffle=True in model.fit. Also you might try replacing the Flatten layer with a GlobalMaxPooling layer. I also recommend using the EarlyStopping callback which monitors validation and halts training if the loss fails to reduce after 'patience' number of consecutive epochs. Setting restore_best_weights=True will load the weights for the epoch with the lowest validation loss so you don't have to save then reload the weights. Set epochs to a large number to ensure this callback activates. Also use the ReduceLROnPlateau to automatically adjust the learning rate based on validation loss.
The code I use is shown below
es=tf.keras.callbacks.EarlyStopping( monitor="val_loss", patience=3,
verbose=1, restore_best_weights=True)
rlronp=tf.keras.callbacks.ReduceLROnPlateau( monitor="val_loss", factor=0.5, patience=1,
verbose=1)
callbacks=[es, rlronp]
In model.fit set callbacks=callbacks. Increase the number of epochs you run to say 100 so that the early stopping callback triggers.
I was verifying with a basic example my TensorFlow (v2.2.0), Cuda (10.1), and cudnn (libcudnn7-dev_7.6.5.32-1+cuda10.1_amd64.deb) and I'm getting weird results...
When running the following example in Keras as shown in https://keras.io/examples/mnist_cnn/ I get the ~99% acc #validation. When I adapt the imports run via the TensorFlow I get only 86%.
I might be forgetting something.
To run using tensorflow:
from __future__ import print_function
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras import backend as K
batch_size = 128
num_classes = 10
epochs = 12
# input image dimensions
img_rows, img_cols = 28, 28
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = tf.keras.utils.to_categorical(y_train, num_classes)
y_test = tf.keras.utils.to_categorical(y_test, num_classes)
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='softmax'))
model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
Sadly, I get the following output:
Epoch 2/12
469/469 [==============================] - 3s 6ms/step - loss: 2.2245 - accuracy: 0.2633 - val_loss: 2.1755 - val_accuracy: 0.4447
Epoch 3/12
469/469 [==============================] - 3s 7ms/step - loss: 2.1485 - accuracy: 0.3533 - val_loss: 2.0787 - val_accuracy: 0.5147
Epoch 4/12
469/469 [==============================] - 3s 6ms/step - loss: 2.0489 - accuracy: 0.4214 - val_loss: 1.9538 - val_accuracy: 0.6021
Epoch 5/12
469/469 [==============================] - 3s 6ms/step - loss: 1.9224 - accuracy: 0.4845 - val_loss: 1.7981 - val_accuracy: 0.6611
Epoch 6/12
469/469 [==============================] - 3s 6ms/step - loss: 1.7748 - accuracy: 0.5376 - val_loss: 1.6182 - val_accuracy: 0.7039
Epoch 7/12
469/469 [==============================] - 3s 6ms/step - loss: 1.6184 - accuracy: 0.5750 - val_loss: 1.4296 - val_accuracy: 0.7475
Epoch 8/12
469/469 [==============================] - 3s 7ms/step - loss: 1.4612 - accuracy: 0.6107 - val_loss: 1.2484 - val_accuracy: 0.7719
Epoch 9/12
469/469 [==============================] - 3s 6ms/step - loss: 1.3204 - accuracy: 0.6402 - val_loss: 1.0895 - val_accuracy: 0.7945
Epoch 10/12
469/469 [==============================] - 3s 6ms/step - loss: 1.2019 - accuracy: 0.6650 - val_loss: 0.9586 - val_accuracy: 0.8097
Epoch 11/12
469/469 [==============================] - 3s 7ms/step - loss: 1.1050 - accuracy: 0.6840 - val_loss: 0.8552 - val_accuracy: 0.8216
Epoch 12/12
469/469 [==============================] - 3s 7ms/step - loss: 1.0253 - accuracy: 0.7013 - val_loss: 0.7734 - val_accuracy: 0.8337
Test loss: 0.7734305262565613
Test accuracy: 0.8337000012397766
Nowhere near 99.25% as when I import Keras.
What am I missing?
Discrepancy in optimiser parameters between keras and tensorflow.keras
So the crux of the issue lies in the different default parameters for the Adadelta optimisers in Keras and Tensorflow. Specifically, the different learning rates. We can see this with a simple check. Using the Keras version of the code, print(keras.optimizers.Adadelta().get_config()) outpus
{'learning_rate': 1.0, 'rho': 0.95, 'decay': 0.0, 'epsilon': 1e-07}
And in the Tensorflow version, print(tf.optimizers.Adadelta().get_config() gives us
{'name': 'Adadelta', 'learning_rate': 0.001, 'decay': 0.0, 'rho': 0.95, 'epsilon': 1e-07}
As we can see, there is a discrepancy between the learning rates for the Adadelta optimisers. Keras has a default learning rate of 1.0 while Tensorflow has a default learning rate of 0.001 (consistent with their other optimisers).
Effects of a higher learning rate
Since the Keras version of the Adadelta optimiser has a larger learning rate, it converges much faster and achieves a high accuracy within 12 epochs, while the Tensorflow Adadelta optimiser requires a longer training time. If you increased the number of training epochs, the Tensorflow model could potentially achieve a 99% accuracy as well.
The fix
But instead of increasing the training time, we can simply initialise the Tensorflow model to behave in a similar way to the Keras model by changing the learning rate of Adadelta to 1.0. i.e.
model.compile(
loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.optimizers.Adadelta(learning_rate=1.0), # Note the new learning rate
metrics=['accuracy'])
Making this change, we get the following performance running on Tensorflow:
Epoch 12/12
60000/60000 [==============================] - 102s 2ms/sample - loss: 0.0287 - accuracy: 0.9911 - val_loss: 0.0291 - val_accuracy: 0.9907
Test loss: 0.029134796149221757
Test accuracy: 0.9907
which is close to the desired 99.25% accuracy.
p.s. Incidentally, it seems that the different default parameters between Keras and Tensorflow is a known issue that was fixed but then reverted:
https://github.com/keras-team/keras/pull/12841 software development is hard.
I'm currently developing a CNN to predict image classification between two classes: Weapons, and not Weapons. The purpose of this project is to be able to detect whether or not a weapon (handgun/rifle) is present in an image.
My issue: No matter what I try, the classifier predicts that there is no weapon in the image. Can you guys find a flaw in my code that might be causing this issue?
I am senior Computer Science Student, but I have very little background the realm of Machine Learning.
Any help is appreciated!
# Initializing the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation='relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation='relu'))
classifier.add(MaxPooling2D(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full connection
classifier.add(Dense(units=128, activation='relu'))
classifier.add(Dense(units=1, activation='sigmoid'))
# Compiling the CNN
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# Part 2 - Fitting the CNN to the images
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1. / 255)
test_datagen = ImageDataGenerator(rescale=1. / 255)
valid_datagen = ImageDataGenerator(rescale=1. / 255)
training_set = train_datagen.flow_from_directory('C:/Users/chill/PycharmProjects/499Actual/venv/data/TrainDataSet/',
target_size=(64, 64),
batch_size=29,
class_mode='binary')
test_set = test_datagen.flow_from_directory('C:/Users/chill/PycharmProjects/499Actual/venv/data/TestDataSet/',
target_size=(64, 64),
batch_size=7,
class_mode='binary')
valid_set = valid_datagen.flow_from_directory('C:/Users/chill/PycharmProjects/499Actual/venv/data/ValidationDataSet/',
target_size=(64, 64),
batch_size=7,
class_mode='binary')
classifier.fit_generator(training_set,
steps_per_epoch=348,
epochs=1,
validation_data=valid_set,
validation_steps=100)
# Part 3 - Making new predictions
import numpy as np
from keras.preprocessing import image
# test_image = image.load_img('C:/Users/chill/PycharmProjects/499Actual/venv/data/TestDataSet/ProbablySoap/P1030135.jpg',
# target_size=(64, 64))
test_image = image.load_img('C:/Users/chill/PycharmProjects/499Actual/venv/data/TestDataSet/Guns/301.jpeg',
target_size=(64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
result = classifier.predict_classes(test_image)
print(result[0][0])
var = training_set.class_indices
if result[0][0] == 1:
prediction = 1
print("Gun!")
else:
prediction = 0
print("Not.")
Disclaimer: "ProbablySoap" is just the set of images that do not contain weapons.
UPDATE
The input image in this scenario is an image containing a weapon.
The output predicts "Not." every time.
UPDATE 2
Here is the output of the code:
Found 348 images belonging to 2 classes.
Found 42 images belonging to 2 classes.
Found 42 images belonging to 2 classes.
Epoch 1/1
1/348 [..............................] - ETA: 1:15 - loss: 0.6915 - accuracy: 0.5517
2/348 [..............................] - ETA: 47s - loss: 0.6994 - accuracy: 0.6724
3/348 [..............................] - ETA: 38s - loss: 0.7130 - accuracy: 0.6897
4/348 [..............................] - ETA: 33s - loss: 0.6565 - accuracy: 0.7155
5/348 [..............................] - ETA: 30s - loss: 0.6496 - accuracy: 0.7103
6/348 [..............................] - ETA: 28s - loss: 0.6384 - accuracy: 0.7241
7/348 [..............................] - ETA: 27s - loss: 0.6301 - accuracy: 0.7340
...
346/348 [============================>.] - ETA: 0s - loss: 0.0940 - accuracy: 0.9628
347/348 [============================>.] - ETA: 0s - loss: 0.0937 - accuracy: 0.9629
348/348 [==============================] - 34s 98ms/step - loss: 0.0935 - accuracy: 0.9630 - val_loss: 0.2081 - val_accuracy: 0.9757
0
Not.
Process finished with exit code 0
I think your problem:
You generated a test_set with scaling:
test_datagen = ImageDataGenerator(rescale=1. / 255)
test_set = test_datagen.flow_from_directory('C:/Users/chill/PycharmProjects/499Actual/venv/data/TestDataSet/',
target_size=(64, 64),
batch_size=7,
class_mode='binary')
But you never use it, you use an imported file from the test directory and use it without scaling:
test_image = image.load_img('C:/Users/chill/PycharmProjects/499Actual/venv/data/TestDataSet/Guns/301.jpeg',
target_size=(64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
That is why the predictor can not classify the later imported image correctly.
Hope it helps.