<jupyter_start><jupyter_text>for i,j in enumerate(val_data):print(j)<jupyter_code>plt.imshow(val_data[43][0][3],cmap='gray')
val_data[43][0][1].shape<jupyter_output><empty_output><jupyter_text>MyModel<jupyter_code>model=Sequential(name='Mymodel')
model.add(Conv2D(32,kernel_size=(3,3),input_shape=[256,256,1],activation='relu',padding='same'))
model.add(MaxPool2D(3,3))

model.add(Conv2D(32,kernel_size=(3,3),activation='relu',padding='same'))
model.add(MaxPool2D(3,3))


model.add(Conv2D(64,kernel_size=(3,3),activation='relu',padding='same'))
model.add(MaxPool2D(3,3))

model.add(Conv2D(64,kernel_size=(3,3),activation='relu',padding='same'))
model.add(MaxPool2D(3,3))


model.add(Conv2D(64,kernel_size=(3,3),activation='relu',padding='same'))
model.add(MaxPool2D(3,3))
model.add(GaussianDropout(0.4))

model.add(Flatten())

model.add(Dense(64,activation='relu'))
model.add(Dense(32,activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(32,activation='relu'))
model.add(Dense(3,activation='softmax'))
model.summary()
opt=Adam(learning_rate=0.001)
los=BinaryCrossentropy()
model.compile(optimizer=opt, loss=los, metrics=["accuracy"])
hist=model.fit(val_data,epochs=0)
ht=hist.history['loss']<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.21498371335504887, 'fraction_numerical': 0.05781758957654723, 'mean_word_length': 18.203125, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '379876', 'license': 'Apache-2.0', 'n_tokens_mistral': 545, 'n_tokens_neox': 512, 'n_words': 30, 'path': 'COVID-19 tester.ipynb', 'repo_name': 'piyush05124/Covid-19-Detection', 'size': '1228'}