Cat ClassifierĀ¶
Run in Google ColabObjective: Train a Deep Neural Network and a Convolutional Neural Network for cat recognition.
A Deep Neural Network (DNN) is a type of neural network with multiple layers between the input and output layers. It is designed to learn complex patterns and relationships in data, and can be used for a wide range of tasks such as image classification, speech recognition, and natural language processing. DNNs can have different architectures, including fully connected layers, convolutional layers, and recurrent layers.
A Convolutional Neural Network (CNN) is a type of DNN that is specifically designed for image and video processing tasks. It uses convolutional layers to extract features from input data, and is particularly useful for tasks such as image classification, object detection, and image segmentation. CNNs are designed to take advantage of the spatial hierarchies and local feature extraction in images, and are often used in computer vision applications.
Import librariesĀ¶
InĀ [1]:
import numpy as np
import h5py
from datetime import datetime
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, ConfusionMatrixDisplay
from keras import Sequential, Input, layers, optimizers, callbacks
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
2025-01-02 15:30:03.479794: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1735853403.492900 205901 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1735853403.496821 205901 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2025-01-02 15:30:03.509589: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Download the datasetĀ¶
InĀ [2]:
%%bash
wget -nc --progress=bar:force:noscroll https://raw.githubusercontent.com/LuisAngelMendozaVelasco/Deep_Learning_Specialization/main/Neural_Networks_and_Deep_Learning/Week2/Labs/datasets/train_catvnoncat.h5 -P /tmp
wget -nc --progress=bar:force:noscroll https://raw.githubusercontent.com/LuisAngelMendozaVelasco/Deep_Learning_Specialization/main/Neural_Networks_and_Deep_Learning/Week2/Labs/datasets/test_catvnoncat.h5 -P /tmp
File '/tmp/train_catvnoncat.h5' already there; not retrieving. File '/tmp/test_catvnoncat.h5' already there; not retrieving.
Load the datasetĀ¶
InĀ [Ā ]:
dataset_train = h5py.File('/tmp/train_catvnoncat.h5', "r")
X_train = dataset_train["train_set_x"][:]
y_train = dataset_train["train_set_y"][:]
dataset_test = h5py.File('/tmp/test_catvnoncat.h5', "r")
X = dataset_test["test_set_x"][:]
y = dataset_test["test_set_y"][:]
X_test, X_validation, y_test, y_validation = train_test_split(X, y, test_size=0.5, random_state=0)
ds_train = tf.data.Dataset.from_tensor_slices((X_train, y_train)).batch(32)
ds_validation = tf.data.Dataset.from_tensor_slices((X_validation, y_validation)).batch(32)
classes = dataset_train["list_classes"][:].astype(str)
I0000 00:00:1735851290.088879 60931 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1430 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1650, pci bus id: 0000:01:00.0, compute capability: 7.5
InĀ [4]:
print("Number of training samples:", X_train.shape[0])
print("Number of validation samples:", X_validation.shape[0])
print("Number of test samples:", X_test.shape[0])
print("Each image has a shape of", X_train.shape[1:])
Number of training samples: 209 Number of validation samples: 25 Number of test samples: 25 Each image has a shape of (64, 64, 3)
Visualize the datasetĀ¶
InĀ [5]:
indexes = np.random.choice(range(0, X_train.shape[0]), size=16, replace=False)
samples = zip(X_train[indexes], y_train[indexes])
fig, axs = plt.subplots(4, 4, figsize=(8, 8))
fig.suptitle('Random samples')
for ax, sample in zip(axs.flatten(), samples):
ax.imshow(sample[0])
ax.set_title(classes[sample[1]])
ax.axis("off")
plt.tight_layout()
plt.show()
Visualize the class distributionĀ¶
InĀ [6]:
labels, sizes = np.unique(y_train, return_counts=True)
fig, ax = plt.subplots()
ax.pie(sizes, textprops={'color': "w", 'fontsize': '12'}, autopct=lambda pct: "{:.2f}%\n({:d})".format(pct, round(pct/100 * sum(sizes))))
ax.legend(["1 (cat)" if label else "0 (non-cat)" for label in labels])
ax.set_title("Class")
plt.show()
Build a Deep Neural NetworkĀ¶
InĀ [7]:
model_DNN = Sequential([Input(shape=(64, 64, 3)),
# Rescale
layers.Rescaling(scale=1 / 255),
# Data augmentation
layers.RandomFlip(mode="horizontal"),
layers.RandomTranslation(height_factor=0.2, width_factor=0.2, fill_mode="nearest"),
layers.RandomRotation(factor=0.2, fill_mode="nearest"),
layers.RandomZoom(height_factor=0.2, width_factor=0.2, fill_mode="nearest"),
# Deep layers
layers.Flatten(),
layers.BatchNormalization(),
layers.Dropout(0.5),
layers.Dense(32, activation="relu"),
layers.BatchNormalization(),
layers.Dropout(0.5),
layers.Dense(64, activation="relu"),
layers.BatchNormalization(),
layers.Dropout(0.5),
layers.Dense(128, activation="relu"),
layers.BatchNormalization(),
layers.Dense(1, activation="sigmoid")])
model_DNN.summary()
Model: "sequential"
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā³āāāāāāāāāāāāāāāāāāāāāāāāā³āāāāāāāāāāāāāāāā ā Layer (type) ā Output Shape ā Param # ā ā”āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā© ā rescaling (Rescaling) ā (None, 64, 64, 3) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā random_flip (RandomFlip) ā (None, 64, 64, 3) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā random_translation ā (None, 64, 64, 3) ā 0 ā ā (RandomTranslation) ā ā ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā random_rotation ā (None, 64, 64, 3) ā 0 ā ā (RandomRotation) ā ā ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā random_zoom (RandomZoom) ā (None, 64, 64, 3) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā flatten (Flatten) ā (None, 12288) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā batch_normalization ā (None, 12288) ā 49,152 ā ā (BatchNormalization) ā ā ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā dropout (Dropout) ā (None, 12288) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā dense (Dense) ā (None, 32) ā 393,248 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā batch_normalization_1 ā (None, 32) ā 128 ā ā (BatchNormalization) ā ā ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā dropout_1 (Dropout) ā (None, 32) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā dense_1 (Dense) ā (None, 64) ā 2,112 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā batch_normalization_2 ā (None, 64) ā 256 ā ā (BatchNormalization) ā ā ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā dropout_2 (Dropout) ā (None, 64) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā dense_2 (Dense) ā (None, 128) ā 8,320 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā batch_normalization_3 ā (None, 128) ā 512 ā ā (BatchNormalization) ā ā ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā dense_3 (Dense) ā (None, 1) ā 129 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā“āāāāāāāāāāāāāāāāāāāāāāāāā“āāāāāāāāāāāāāāāā
Total params: 453,857 (1.73 MB)
Trainable params: 428,833 (1.64 MB)
Non-trainable params: 25,024 (97.75 KB)
Create a custom callbackĀ¶
InĀ [8]:
class CustomVerbose(callbacks.Callback):
def __init__(self, epochs_to_show):
self.epochs_to_show = epochs_to_show
def on_epoch_begin(self, epoch, logs=None):
if epoch in self.epochs_to_show:
self.epoch_start_time = datetime.now()
def on_epoch_end(self, epoch, logs=None):
if epoch in self.epochs_to_show:
self.epoch_stop_time = datetime.now()
print(f"Epoch {epoch+1}/{self.epochs_to_show[-1] + 1}")
print(f"\telapsed time: {(self.epoch_stop_time - self.epoch_start_time).total_seconds():.3f}s - accuracy: {logs['binary_accuracy']:.4f} - loss: {logs['loss']:.4f} - val_accuracy: {logs['val_binary_accuracy']:.4f} - val_loss: {logs['val_loss']:.4f}")
Compile and train the modelĀ¶
InĀ [9]:
model_DNN.compile(optimizer="adam", loss='binary_crossentropy', metrics=['binary_accuracy'])
epochs = 1000
epochs_to_show = [0] + [i for i in range(int(epochs/10)-1, epochs, int(epochs/10))]
custom_verbose = CustomVerbose(epochs_to_show)
early_stopping = callbacks.EarlyStopping(monitor='val_loss', patience=int(epochs/10), verbose=1)
history_DNN = model_DNN.fit(ds_train, epochs=epochs, verbose=0, validation_data=ds_validation, callbacks=[custom_verbose, early_stopping])
Epoch 1/1000 elapsed time: 4.370s - accuracy: 0.5215 - loss: 0.9016 - val_accuracy: 0.3600 - val_loss: 1.1929 Epoch 100/1000 elapsed time: 0.046s - accuracy: 0.6938 - loss: 0.5545 - val_accuracy: 0.5200 - val_loss: 0.7159 Epoch 200/1000 elapsed time: 0.056s - accuracy: 0.6794 - loss: 0.5162 - val_accuracy: 0.6800 - val_loss: 0.6426 Epoch 300/1000 elapsed time: 0.053s - accuracy: 0.7177 - loss: 0.4660 - val_accuracy: 0.6800 - val_loss: 0.5633 Epoch 400/1000 elapsed time: 0.051s - accuracy: 0.7464 - loss: 0.4551 - val_accuracy: 0.6400 - val_loss: 0.5554 Epoch 449: early stopping
InĀ [10]:
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
ax1.plot(history_DNN.history['binary_accuracy'])
ax1.plot(history_DNN.history['val_binary_accuracy'])
ax1.set_xlabel("Epochs")
ax1.set_ylabel("Accuracy")
ax1.legend(["Training", "Validation"])
ax2.plot(history_DNN.history['loss'])
ax2.plot(history_DNN.history['val_loss'])
ax2.set_xlabel("Epochs")
ax2.set_ylabel("Loss")
ax2.legend(["Training", "Validation"])
plt.show()
Evaluate the modelĀ¶
InĀ [11]:
indexes = np.random.choice(range(0, X_test.shape[0]), size=16, replace=False)
fig, axs = plt.subplots(4, 4, figsize=(8, 8))
fig.suptitle('Random samples')
for image, ax in zip(X_test[indexes], axs.flatten()):
prediction_proba = model_DNN.predict(np.expand_dims(image, axis=0), verbose=0)
ax.imshow(image)
ax.set_title("Prediction:" + classes[int(prediction_proba.squeeze().round())])
ax.axis("off")
plt.tight_layout()
plt.show()
InĀ [12]:
y_pred = []
for image in X_test:
prediction_proba = model_DNN.predict(np.expand_dims(image, axis=0), verbose=0)
y_pred.append(int(prediction_proba.squeeze().round()))
print(classification_report(y_test, y_pred, digits=4))
ConfusionMatrixDisplay.from_predictions(y_test, y_pred, display_labels=["0 (non-cat)", "1 (cat)"])
plt.grid(False)
plt.show()
precision recall f1-score support
0 0.5455 0.7500 0.6316 8
1 0.8571 0.7059 0.7742 17
accuracy 0.7200 25
macro avg 0.7013 0.7279 0.7029 25
weighted avg 0.7574 0.7200 0.7286 25
Build a Convolutional Neural NetworkĀ¶
InĀ [20]:
model_CNN = Sequential([Input(shape=(64, 64, 3)),
# Rescale
layers.Rescaling(scale=1 / 255),
# Data augmentation
layers.RandomFlip(mode="horizontal"),
layers.RandomTranslation(height_factor=0.2, width_factor=0.2, fill_mode="nearest"),
layers.RandomRotation(factor=0.2, fill_mode="nearest"),
layers.RandomZoom(height_factor=0.2, width_factor=0.2, fill_mode="nearest"),
# Convolutional layers
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
# Deep layers
layers.Flatten(),
layers.BatchNormalization(),
layers.Dropout(0.5),
layers.Dense(128, activation="relu"),
layers.BatchNormalization(),
layers.Dense(1, activation="sigmoid")])
model_CNN.summary()
Model: "sequential_3"
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā³āāāāāāāāāāāāāāāāāāāāāāāāā³āāāāāāāāāāāāāāāā ā Layer (type) ā Output Shape ā Param # ā ā”āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā© ā rescaling_3 (Rescaling) ā (None, 64, 64, 3) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā random_flip_3 (RandomFlip) ā (None, 64, 64, 3) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā random_translation_3 ā (None, 64, 64, 3) ā 0 ā ā (RandomTranslation) ā ā ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā random_rotation_3 ā (None, 64, 64, 3) ā 0 ā ā (RandomRotation) ā ā ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā random_zoom_3 (RandomZoom) ā (None, 64, 64, 3) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā conv2d_6 (Conv2D) ā (None, 64, 64, 16) ā 448 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā max_pooling2d_6 (MaxPooling2D) ā (None, 32, 32, 16) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā conv2d_7 (Conv2D) ā (None, 32, 32, 32) ā 4,640 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā max_pooling2d_7 (MaxPooling2D) ā (None, 16, 16, 32) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā conv2d_8 (Conv2D) ā (None, 16, 16, 64) ā 18,496 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā max_pooling2d_8 (MaxPooling2D) ā (None, 8, 8, 64) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā flatten_3 (Flatten) ā (None, 4096) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā batch_normalization_8 ā (None, 4096) ā 16,384 ā ā (BatchNormalization) ā ā ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā dropout_5 (Dropout) ā (None, 4096) ā 0 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā dense_8 (Dense) ā (None, 128) ā 524,416 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā batch_normalization_9 ā (None, 128) ā 512 ā ā (BatchNormalization) ā ā ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāāāāāāāāāāā¼āāāāāāāāāāāāāāāā¤ ā dense_9 (Dense) ā (None, 1) ā 129 ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā“āāāāāāāāāāāāāāāāāāāāāāāāā“āāāāāāāāāāāāāāāā
Total params: 565,025 (2.16 MB)
Trainable params: 556,577 (2.12 MB)
Non-trainable params: 8,448 (33.00 KB)
Compile and train the modelĀ¶
InĀ [21]:
model_CNN.compile(optimizer=optimizers.Adam(1e-5), loss='binary_crossentropy', metrics=['binary_accuracy'])
epochs = 1000
epochs_to_show = [0] + [i for i in range(int(epochs/10)-1, epochs, int(epochs/10))]
custom_verbose = CustomVerbose(epochs_to_show)
early_stopping = callbacks.EarlyStopping(monitor='val_loss', patience=epochs/10, verbose=1)
history_CNN = model_CNN.fit(ds_train, epochs=epochs, verbose=0, validation_data=ds_validation, callbacks=[custom_verbose, early_stopping])
Epoch 1/1000 elapsed time: 2.571s - accuracy: 0.5215 - loss: 0.8841 - val_accuracy: 0.3200 - val_loss: 0.7039 Epoch 100/1000 elapsed time: 0.059s - accuracy: 0.6986 - loss: 0.5975 - val_accuracy: 0.7600 - val_loss: 0.5993 Epoch 200/1000 elapsed time: 0.056s - accuracy: 0.7512 - loss: 0.4966 - val_accuracy: 0.7200 - val_loss: 0.5427 Epoch 300/1000 elapsed time: 0.067s - accuracy: 0.7799 - loss: 0.4475 - val_accuracy: 0.7600 - val_loss: 0.5031 Epoch 400/1000 elapsed time: 0.063s - accuracy: 0.8278 - loss: 0.3617 - val_accuracy: 0.6800 - val_loss: 0.4953 Epoch 403: early stopping
InĀ [22]:
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
ax1.plot(history_CNN.history['binary_accuracy'])
ax1.plot(history_CNN.history['val_binary_accuracy'])
ax1.set_xlabel("Epochs")
ax1.set_ylabel("Accuracy")
ax1.legend(["Training", "Validation"])
ax2.plot(history_CNN.history['loss'])
ax2.plot(history_CNN.history['val_loss'])
ax2.set_xlabel("Epochs")
ax2.set_ylabel("Loss")
ax2.legend(["Training", "Validation"])
plt.show()
Evaluate the modelĀ¶
InĀ [23]:
indexes = np.random.choice(range(0, X_test.shape[0]), size=16, replace=False)
fig, axs = plt.subplots(4, 4, figsize=(8, 8))
fig.suptitle('Random samples')
for image, ax in zip(X_test[indexes], axs.flatten()):
prediction_proba = model_CNN.predict(np.expand_dims(image, axis=0), verbose=0)
ax.imshow(image)
ax.set_title("Prediction: " + classes[int(prediction_proba.squeeze().round())])
ax.axis("off")
plt.tight_layout()
plt.show()
InĀ [24]:
y_pred = []
for image in X_test:
prediction_proba = model_CNN.predict(np.expand_dims(image, axis=0), verbose=0)
y_pred.append(int(prediction_proba.squeeze().round()))
print(classification_report(y_test, y_pred, digits=4))
ConfusionMatrixDisplay.from_predictions(y_test, y_pred, display_labels=["0 (non-cat)", "1 (cat)"])
plt.grid(False)
plt.show()
precision recall f1-score support
0 0.4667 0.8750 0.6087 8
1 0.9000 0.5294 0.6667 17
accuracy 0.6400 25
macro avg 0.6833 0.7022 0.6377 25
weighted avg 0.7613 0.6400 0.6481 25
